Cancer Genomics Research Research Articles

A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.

In cancer genomics research, gene expressions provide clues to gene regulations implicating patients' risk of survival. Gene expressions, however, fluctuate due to noises arising internally and externally, making their use to infer gene associations, hence regulation mechanisms, problematic. Here, we develop a new regression approach to model gene association networks while considering uncertain biological noises. In a series of simulation experiments accounting for varying levels of biological noises, the new method was shown to be robust and perform better than conventional regression methods, as judged by a number of statistical measures on unbiasedness, consistency and accuracy. Application to infer gene associations in germinal-center B cells led to the discovery of a three-by-two regulatory motif gene expression and a three-gene prognostic signature for diffuse large B-cell lymphoma.

Taking the Measure of Cancer Genomically

Genetic Engineering & Biotechnology NewsVol. 43, No. 3 Cancer ResearchTaking the Measure of Cancer GenomicallyBesides collecting and freely sharing cancer genome data, research organizations and consortia are integrating genomic and clinical information and offering analytical toolsMaryAnn LabantMaryAnn LabantSearch for more papers by this authorPublished Online:8 Mar 2023https://doi.org/10.1089/gen.43.03.08AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 43Issue 3Mar 2023 Information© 2023 by GEN PublishingTo cite this article:MaryAnn Labant.Taking the Measure of Cancer Genomically.Genetic Engineering & Biotechnology News.Mar 2023.24-28.http://doi.org/10.1089/gen.43.03.08Published in Volume: 43 Issue 3: March 8, 2023PDF download

Priorities to Promote Participant Engagement in the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network.

Engaging diverse populations in cancer genomics research is of critical importance and is a fundamental goal of the NCI Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. Established as part of the Cancer Moonshot, PE-CGS is a consortium of stakeholders including clinicians, scientists, genetic counselors, and representatives of potential study participants and their communities. Participant engagement is an ongoing, bidirectional, and mutually beneficial interaction between study participants and researchers. PE-CGS sought to set priorities in participant engagement for conducting the network's research. PE-CGS deliberatively engaged its stakeholders in the following four-phase process to set the network's research priorities in participant engagement: (i) a brainstorming exercise to elicit potential priorities; (ii) a 2-day virtual meeting to discuss priorities; (iii) recommendations from the PE-CGS External Advisory Panel to refine priorities; and (iv) a virtual meeting to set priorities. Nearly 150 PE-CGS stakeholders engaged in the process. Five priorities were set: (i) tailor education and communication materials for participants throughout the research process; (ii) identify measures of participant engagement; (iii) identify optimal participant engagement strategies; (iv) understand cancer disparities in the context of cancer genomics research; and (v) personalize the return of genomics findings to participants. PE-CGS is pursuing these priorities to meaningfully engage diverse and underrepresented patients with cancer and posttreatment cancer survivors as participants in cancer genomics research and, subsequently, generate new discoveries. Data from PE-CGS will be shared with the broader scientific community in a manner consistent with participant informed consent and community agreement.

Promoting patient engagement in cancer genomics research programs: An environmental scan.

Background: A national priority in the United States is to promote patient engagement in cancer genomics research, especially among diverse and understudied populations. Several cancer genomics research programs have emerged to accomplish this priority, yet questions remain about the meaning and methods of patient engagement. This study explored how cancer genomics research programs define engagement and what strategies they use to engage patients across stages in the conduct of research. Methods: An environmental scan was conducted of cancer genomics research programs focused on patient engagement. Research programs were identified and characterized using materials identified from publicly available sources (e.g., websites), a targeted literature review, and interviews with key informants. Descriptive information about the programs and their definitions of engagement, were synthesized using thematic analysis. The engagement strategies were synthesized and mapped to different stages in the conduct of research, including recruitment, consent, data collection, sharing results, and retention. Results: Ten research programs were identified, examples of which include the Cancer Moonshot Biobank, the MyPART Network, NCI-CONNECT, and the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. All programs aimed to include understudied or underrepresented populations. Based on publicly available information, four programs explicitly defined engagement. These definitions similarly characterized engagement as being interpersonal, reciprocal, and continuous. Five general strategies of engagement were identified across the programs: 1) digital (such as websites) and 2) non-digital communications (such as radio broadcasts, or printed brochures); 3) partnering with community organizations; 4) providing incentives; and 5) affiliating with non-academic medical centers. Digital communications were the only strategy used across all stages of the conduct of research. Programs tailored these strategies to their study goals, including overcoming barriers to research participation among diverse populations. Conclusion: Programs studying cancer genomics are deeply committed to increasing research participation among diverse populations through patient engagement. Yet, the field needs to reach a consensus on the meaning of patient engagement, develop a taxonomy of patient engagement measures in cancer genomics research, and identify optimal strategies to engage patients in cancer genomics. Addressing these needs could enable patient engagement to fulfill its potential and accelerate the pace of cancer genomic discoveries.

Malignant testicular unclassified sex cord stromal tumor: a case report

BackgroundMost testicular tumors are germ cell tumors; sex cord stromal tumors are infrequent, accounting for only 3–5% of testicular tumors. Unclassified sex cord stromal tumors are extremely rare. Generally, 10% of sex cord stromal tumors are malignant. We report a case of malignant unclassified sex cord stromal tumor with retroperitoneal lymph node metastasis at first visit and a corresponding literature review.Case presentationA 72-year-old Japanese man visited our department primarily for indolent right scrotum enlargement in September 2020. Blood biochemistry examination, urinalysis, and tumor markers (alpha-fetoprotein, human chorionic gonadotropin, and lactate dehydrogenase) showed no abnormal findings. Contrast-enhanced computed tomography showed enlarged para-aortic lymph node (18 × 16 and 10 × 102 mm); a 50 × 452 mm mass with uneven contents was found in the right testicle. The patient underwent inguinal orchiectomy in September 2020. As per immunohistochemistry, the tumor cells were diffusely positive for SF-1 and Ki-67, partially positive for inhibin, and negative for CAM5.2, CK7, CK20, C-KIT, CD30, LCA, GATA-3, TTF-1, and PAX8. Calretinin was expressed in approximately 5% of tumor cells; thus, sex cord/gonadal stroma components were considered to be involved. The final pathological diagnosis was unclassified malignant sex cord stromal tumor. The patient was diagnosed with pT1, N1, M0, S0, and tumor–node–metastasis stage IIA disease. The patient received postoperative chemotherapy with four courses of etoposide and cisplatin therapy from November 2020. Post-chemotherapeutic computed tomography showed new metastatic lesions including lung, liver, pancreas, and para-aortic lymphadenopathy, which increased in size. Disease progression was observed. Cancer genome research was performed using the OncoGuide National Cancer Center oncopanel system; however, no gene mutation for which the drug could be expected to be effective was found. The patient opted for best supportive care at a nearby hospital and died from cancer progression in January 2022.ConclusionWe encountered a case of malignant testicular unclassified sex cord stromal tumor pathologically diagnosed as testicular tumor with retroperitoneal lymph node metastasis in a patient who underwent inguinal orchiectomy. Future data collection is necessary to establish multimodality therapy for malignant testicular unclassified sex cord stromal tumor.

Impact of Clinical Data Veracity on Cancer Genomic Research.

Genomic analysis of tumors is transforming our understanding of cancer. However, although a great deal of attention is paid to the accuracy of the cancer genomic data itself, less attention has been paid to the accuracy of the associated clinical information that renders the genomic data useful for research. In this brief communication, we suggest that omissions and errors in clinical annotations have a major impact on the interpretation of cancer genomic data. We describe our discovery of annotation omissions and errors when reviewing an already carefully annotated colorectal cancer gene expression dataset from our laboratory. The potential importance of clinical annotation omissions and errors was then explored using simulation analyses with an independent genomic dataset. We suggest that the completeness and veracity of clinical annotations accompanying cancer genomic data require renewed focus by the oncology research community, when planning new collections and when interpreting existing cancer genomic data.

S2163 Understanding Prolonged Survival in Advanced Colorectal Cancer Using Genomics: A Case Series

Introduction: Recent progress in molecular biology and cancer genome research had opened a new venue for clinical research and changed treatment paradigm for metastatic colorectal cancer. Case Description/Methods: Case 1: A 61-year -old Hispanic male underwent colonoscopy which revealed an mass in the proximal ascending colon with biopsy showing moderately differentiated adenocarcinoma. The patient underwent a right hemicolectomy in 2012. He subsequently completed chemotherapy with mFOLFOX-6. Further treatment was declined by patient. Follow up chest CT scan in April 2018, uncovered progression of pulmonary nodules (Figure, Panel 1). Patient was put on FOLFIRI/bevacizumab chemotherapy. NGS study showed following results; MS stable; TMB 4 mutations/Mb (low); APCR1450, BRAF D594N, FAM 123B K761, SOX9 G225fs, and TP53 R175H.The patient has stable disease on follow up. Case 2: A 73-year-old Hispanic male underwent colonoscopy showing a obstructing mass in the sigmoid colon. Biopsy showed moderately differentiated adenocarcinoma in December 2013. Patient underwent sigmoidectomy. Liver masses were found on repeat CT abdomen. The NGS reported as follows; MS-Stable, TMB 1 muts/Mb(low), APC T282fs*12/P1453fs*20, CDK8 amplification, fms like tyrosine kinase 3 (FLT3) amplification, KRAS amplification and TP53 V147D. It also detected wild type KRAS mutations in exons 2, 3 and 4. The patient was started on chemotherapy with mFOLFOX. Follow- up studies show stable disease. Case 3: A 73-year-old Hispanic male underwent colonoscopy showed a completely obstructing mass in the sigmoid colon. Biopsy showed moderately differentiated adenocarcinoma in June 2012. The tumor cells have KRAS G12D mutation in in codon 12 of KRAS. He underwent a left hemicolectomy. He completed mFOLFOX6.PET scan was done and showed a right upper lobe mass consistent with metastatic cancer. He was started on FOLFIRI/ bevacizumab. NGS reported as follows; MS-Stable, TMB 1 muts/Mb(low), APC V1452fs*21, and ATM S47fs*11. Bone Scan intense uptake in the right distal tibia (Figure, Panel 2). MRI of the right lower extremity showed a lesion in the distal tibia. He was then switched to irinotecan and bevacizumab regimen. He has stable disease. Discussion: Identification of genomic signature is key to understanding the molecular mechanism of CRC and the development of novel therapeutics.Figure 1.: Panel 1 (left) shows CT Chest for Case 1 with multiple pulmonary nodules few with cavitation, largest 9mm. Panel 2 (right) shows Bone Scan for Case 3 in February 2021 with intense uptake in the right distal tibia.

TSomVar: a tumor-only somatic and germline variant identification method with random forest.

Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.

Abstract 3108: Preclinical studies of chemotherapies and targeted therapies to address gastric cancer disparities

Abstract Gastric cancer (GC) is one of the most common malignant tumors of the digestive system, and its prognosis remains poor because most tumors were detected in late stages and the 5-year survival is less than 5%. The Cancer Genome Atlas (TCGA) study found that 70% of all GCs have mutations that can be targeted with existing drugs, yet only four molecularly-guided therapies have been approved. GCs have strong disparities in incidence, mortality and survival that disproportionately affect different populations of color. No large-scale cancer genomic research has been carried out in U.S. minority populations. GC molecular data from U.S. minority residents is crucial to improve outcomes, as genomic phenotypes have been associated with differential prognosis and response to treatments. To our knowledge, minority patient-derived models have not been used to test GC therapies. In this study, several organoids lines from minority patients were established and used as an in vitro platform for screening effective therapies. After genomic data analyses, six minority patient-derived organoids with or without cell cycle regulator or PI3K/AKT/mTOR pathway gene alterations were tested for responses to first-line therapies, targeted drugs and combination treatments. Among the 24 PDX models established, the efficacy study of PDX model PDX015 which has amplifications of CDK and AKT genes showed the targeting therapy (a CDKi) can effectively prolong overall survival of mice bearing xenograft treated with the CDKi (60 days), compared with those mice in control group (22 days). In addition, an AKT inhibitor was found significantly inhibited tumor growth in CDKi-resistant mice treated with the CDKi/AKTi combination. In conclusion, investigating the efficacy of targeted therapies in these U.S. minorities will empower precision medicine applications in GC patients and improve GC treatment outcomes. Minority-patient derived tumor models have great potential in preclinical studies of chemotherapies and targeted therapies to address gastric cancer disparities. Citation Format: Hongyong Zhang, Ana Patricia Estrada-Florez, Paul Lott, Ted William Toal, Alexa Morales-Arana, Alma Poceros-Coba, UCaMP Consortium, Ai-Hong Ma, Nicole Britney Halmai, Luis Carvajal-Carmona. Preclinical studies of chemotherapies and targeted therapies to address gastric cancer disparities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3108.

Abstract 5004: Integrative quality control of cancer somatic mutations with CaMutQC

Abstract The quality control of cancer somatic mutations is an essential step for eliminating false positive mutations from technical bias, and selecting key mutation candidates plays a crucial role in both downstream translation research and personal medical decisions. Existing tools, with complicated parameters and changeable filtering standards, may not be suitable specifically for cancer somatic mutations. In addition, previous genomic studies usually adopted different filtering criteria for processing raw sequencing datasets, which greatly restricts the efficiency of integrative exploration for cancer genomics. In this study, we presented CaMutQC, a heuristic cancer somatic mutations (CAMs) quality control and filtration package for cancer genomic studies. CaMutQC provides two schemes, including filtration of false positive mutations generated by technical issues, and the screening of candidate cancer somatic mutations from single or multiple VEP-annotated files in VCF format. Specifically, for quality control, instead of directly discarding CAMs that failed to meet the thresholds of parameters collected from classic studies, CaMutQC labels them with customized tags to represent the filtering type, enabling portable subsequent analyses. And a vivid and well-structured filter report is generated after filtration or selection. In addition, we proved by applying an updated mutation QC strategy, which takes the union of CaMutQC-filtered cancer variants from multiple variant callers (eg. MuTect, MuSE and VarScan2) on published datasets that, CaMutQC can not only reduce the false positive CAMS, but also greatly rescue the false negative CAMS from each single caller or the overlap of multiple tools. In summary, in this study, for the first time, we systematically implemented the parameters and criteria of CAMs quality control from published studies into the CaMutQC package, which we believe it would be a valuable tool for cancer genomic research. CaMutQC is implemented in R and is available at https://github.com/likelet/CaMutQC under the GPL-v3 license. Citation Format: Xin Wang, Jian Ren, Qi Zhao. Integrative quality control of cancer somatic mutations with CaMutQC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5004.

A multidisciplinary approach to optimize primary prostate cancer biobanking.

Biobanking tissue of high quality and fidelity is imperative for cancer genomics research. Since it is a challenging process, we sought to develop a protocol that improves the fidelity and quantity of biobanked primary prostate cancer (CaP) tissue. We conducted a pilot study evaluating pathologic concordance of biobanked tissue and the radical prostatectomy specimen using either standard protocol (SP) vs. next-generation protocol (NGP). There were no significant differences in clinical and pathologic characteristics (age, BMI, preoperative PSA, prostate weight, race, final prostatectomy Gleason score, or pathologic tumor and nodal stages) between the two protocol arms. Utilization of the NGP compared to the standard protocol resulted in a significantly higher rate of pathologic concordance between the biobanked and RP specimens (61.8% vs. 37.9%, P = 0.0231) as well as a nearly two-fold increase in the amount of biobanked tumor tissue (330 mm3 vs. 174 mm3, P < 0.001). When looking at relevant clinical and pathologic characteristics, NGP was associated with pathologic concordance on both univariate [OR 2.65 (95% CI 1.13-6.21), P = 0.025] and multivariate analysis [OR 3.11 (95% CI 1.09-8.88), P = 0.034]. Our study validates the NGP as a multidisciplinary approach for improving the fidelity and amount of biobanked primary CaP tissue for future studies. Given the challenges to banking tissue from primary CaP as tumors are often difficult to visualize grossly and are frequently multifocal, optimizing the fidelity and volume of biobanked tissue is an important step forward to improve the generalizability of genomic data as we move towards precision medicine.

Cellular and Molecular Profiling of Tumor Microenvironment and Early-Stage Lung Cancer.

Lung cancers are broadly divided into two categories: non-small-cell lung carcinoma (NSCLC), which accounts for 80–85% of all cancer cases, and small-cell lung carcinoma (SCLC), which covers the remaining 10–15%. Recent advances in cancer biology and genomics research have allowed an in-depth characterization of lung cancers that have revealed new therapy targets (EGFR, ALK, ROS, and KRAS mutations) and have the potential of revealing even more biomarkers for diagnostic, prognostic, and targeted therapies. A new source of biomarkers is represented by non-coding RNAs, especially microRNAs (miRNAs). MiRNAs are short non-coding RNA sequences that have essential regulatory roles in multiple cancers. Therefore, we aim to investigate the tumor microenvironment (TME) and miRNA tumor profile in a subset of 51 early-stage lung cancer samples (T1 and T2) to better understand early tumor and TME organization and molecular dysregulation. We analyzed the immunohistochemistry expression of CD4 and CD8 as markers of the main TME immune populations, E-cadherin to evaluate early-stage epithelial-to-mesenchymal transition (EMT), and p53, the main altered tumor suppressor gene in lung cancer. Starting from these 4 markers, we identified and validated 4 miRNAs that target TP53 and regulate EMT that can be further investigated as potential early-stage lung cancer biomarkers.

Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning.

Healthcare disparities in multiethnic medical data is a major challenge; the main reason lies in the unequal data distribution of ethnic groups among data cohorts. Biomedical data collected from different cancer genome research projects may consist of mainly one ethnic group, such as people with European ancestry. In contrast, the data distribution of other ethnic races such as African, Asian, Hispanic, and Native Americans can be less visible than the counterpart. Data inequality in the biomedical field is an important research problem, resulting in the diverse performance of machine learning models while creating healthcare disparities. Previous researches have reduced the healthcare disparities only using limited data distributions. In our study, we work on fine-tuning of deep learning and transfer learning models with different multiethnic data distributions for the prognosis of 33 cancer types. In previous studies, to reduce the healthcare disparities, only a single ethnic cohort was used as the target domain with one major source domain. In contrast, we focused on multiple ethnic cohorts as the target domain in transfer learning using the TCGA and MMRF CoMMpass study datasets. After performance comparison for experiments with new data distributions, our proposed model shows promising performance for transfer learning schemes compared to the baseline approach for old and new data distributation experiments.

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5′-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.

Advances of mutation signature analyses in cancer genome research

Advances of mutation signature analyses in cancer genome research

Abstract PO-085: A mixed-methods study of minority recruitment for cancer genomics research at a large urban safety net hospital

Abstract Background: Racial and ethnic minorities are underrepresented in cancer genomics research, and advancements in personalized medicine without inclusion of minority groups is likely to further exacerbate disparities in care and treatment outcomes. Prior research has demonstrated successful recruitment of minority populations for biospecimen donation through community-based approaches, physician engagement, culturally appropriate education, and on-site services. However, there is a significant gap in the literature regarding practical, low-cost interventions to maximize enrollment in biospecimen research studies at hospitals serving a diverse patient population. Objective: This study surveys eligible participants for cancer biospecimen research and aims to intervene on barriers to enrollment. Methods: Between January 2021 and June 2021, participants eligible for four different genomics studies were surveyed regarding reasons for consenting to biospecimen donation as well as barriers to participating among those who declined. The survey consists of six multi-part questions for a total of thirteen questions and requires an average of five minutes to complete. Results: Preliminary results include survey responses from 30 participants. Fourteen participants self-identified as Black or African American, twelve as White, two as Asian, and two as other. One participant self-identified as Hispanic. The majority of participants (73%) consented to biobank specimen collection including 50% of Black participants, 92% of White participants, and 100% of Asian and other participants. Of the eight participants who did not consent to donation, seven were Black. Among nine participants who reported that the study was recommended by their provider, all but one consented to donation. The majority of participants who consented to biospecimen collection reported contributing important information to medical science (73%) and potentially helping others with similar conditions in the future (68%) as reasons for donation. One participant who declined biospecimen donation reported that he would have felt less anxious if clinical staff had been present to explain the study. Two participants recommended including family members and other caregivers in the consent process, especially for older adults. One participant indicated difficulty reading through the consent form while another reported desire for more information “on the type of research” that would be conducted on the biospecimens. Conclusion: Our findings suggest that a majority of patients will consent to biospecimen collection, however Black patients are more likely to decline, and provider engagement is helpful. We found that our consent process can be improved with greater physician engagement, inclusion of family members and caregivers, and concise, patient-oriented presentation of information. Future work will involve implementation of interventions based on these findings. Citation Format: Tina Zhang, Nina Modanlo, David Li, Kiana Mahdaviani, Naomi Ko. A mixed-methods study of minority recruitment for cancer genomics research at a large urban safety net hospital [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-085.

Prostate Cancer Genomics Research Disparities in Africa: Advancing Knowledge in Resource Constrained Settings.

Prostate cancer disproportionately affects men of African descent and it is estimated that Africa will bear the highest disease burden in the next decade. Underlying genomic factors may contribute to prostate cancer disparities; however, it is unclear whether Africa has prioritised genomics research toward addressing these disparities. A Pubmed review was performed of publications spanning a 15-year period, with specific focus on prostate cancer genomics research that included samples from Africa and investigators in Africa. Data are presented on research publications from Africa relative to similar publications from different geographical regions, and more specifically, the extent of disparities and the contributions to prostate cancer knowledge as a result of genomics research that included African samples and African institutions. Limited publication output may reflect the infrastructure and funding challenges in Africa. Widespread cooperation should be fostered by sharing capacity and leveraging existing expertise to address the growing cancer burden facing the continent.

Modern diagnostic technologies and molecular therapies in oncogenetics

Advances in cancer genomics research in recent decades have shown that different types of genomic changes cause neoplasms. The last genome was sequenced in 2018, and the information obtained from this technology brought valuable data for research and personalized management of these patients. By the second half of the last decade, it was becoming clearer and clearer that, although the number of genetic changes that could be achieved through targeted therapies was expanding rapidly, there was no genomic technology to identify them all at once. The mismatch repair (MMR) is represented by epigenetic mutations or phenomena in one of the genes of the MMR complex (most commonly MLH1, MSH2, MSH6 and PMS2). As a result, there is an accumulation of DNA abnormalities, especially in the mononucleotides repeats (MNRs) or dinucleotide repeats in the genome, which are known as microsatellites. A correct genetic diagnosis of the cancer patient helps to make the whole case management and directs the multidisciplinary medical team towards targeted molecular therapies.

Fibroblast growth factor (FGF), FGF receptor (FGFR), and cyclin D1 (CCND1) DNA methylation in head and neck squamous cell carcinomas is associated with transcriptional activity, gene amplification, human papillomavirus (HPV) status, and sensitivity to tyrosine kinase inhibitors

BackgroundDysregulation of fibroblast growth factor receptor (FGFR) signaling pathway has been observed in head and neck squamous cell carcinoma (HNSCC) and is a promising therapeutic target for selective tyrosine kinase inhibitors (TKIs). Potential predictive biomarkers for response to FGFR-targeted therapies are urgently needed. Understanding the epigenetic regulation of FGF pathway related genes, i.e. FGFRs, FGFs, and CCND1, could enlighten the way towards biomarker-selected FGFR-targeted therapies.MethodsWe performed DNA methylation analysis of the encoding genes FGFR1, FGFR2, FGFR3, FGFR4, FGF1-14, FGF16-23, and CCND1 at single CpG site resolution (840 CpG sites) employing The Cancer Genome Research Atlas (TCGA) HNSCC cohort comprising N = 530 tumor tissue and N = 50 normal adjacent tissue samples. We correlated DNA methylation to mRNA expression with regard to human papilloma virus (HPV) and gene amplification status. Moreover, we investigated the correlation of methylation with sensitivity to the selective FGFR inhibitors PD 173074 and AZD4547 in N = 40 HPV(−) HNSCC cell lines.ResultsWe found sequence-contextually nuanced CpG methylation patterns in concordance with epigenetically regulated genes. High methylation levels were predominantly found in the promoter flank and gene body region, while low methylation levels were present in the central promoter region for most of the analyzed CpG sites. FGFRs, FGFs, and CCND1 methylation differed significantly between tumor and normal adjacent tissue and was associated with HPV and gene amplification status. CCND1 promoter methylation correlated with CCND1 amplification. For most of the analyzed CpG sites, methylation levels correlated to mRNA expression in tumor tissue. Furthermore, we found significant correlations of DNA methylation of specific CpG sites with response to the FGFR1/3–selective inhibitors PD 173074 and AZD4547, predominantly within the transcription start site of CCND1.ConclusionsOur results suggest an epigenetic regulation of CCND1, FGFRs, and FGFs via DNA methylation in HNSCC and warrants further investigation of DNA methylation as a potential predictive biomarker for response to selective FGFR inhibitors in clinical trials.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.