Statistical genomics in rare cancer

Identification of barriers to implementation of precision oncology in patients with rare cancers

Established treatment options for rare cancers are limited by the small number of patients. The current comprehensive genomic profiling (CGP) testing might not fully exploit opportunities for precision oncology in patients with rare cancers. Therefore, we aimed to explore the factors associated with CGP testing utility in rare cancers and identify barriers to implementing precision oncology. Patients who underwent CGP testing at our institution between September 2019 and June 2021 were enrolled in this retrospective study. Based on their results, the patients received molecularly targeted drugs or immune checkpoint inhibitors. Univariate and multivariate analyses evaluated the association between patient characteristics and the proportion of patients receiving molecularly targeted drugs. Overall, 790 patients underwent CGP testing. Among them, 333 patients with rare cancers were identified, of whom 278 (83.5%) had actionable genomic alterations, 127 (38.1%) had druggable genomic alterations, and 25 (7.5%) received genomically matched therapy. The proportion of patients receiving molecularly targeted drugs was significantly higher among those with treatment options with evidence levels A-D (8.7%) than those without treatment options with evidence levels A-D (2.9%). A potential barrier to CGP testing utility in rare cancers is the limited number of molecularly targeted drugs with clinical evidence. We propose that CGP testing be performed in patients with rare cancers who have treatment options with evidence levels A-D to maximize CGP testing utility in real-world practice.

Longitudinal Natural History Study of Children and Adults with Rare Solid Tumors: Initial Results for First 200 Participants

Understanding of tumor biology and identification of effective therapies is lacking for many rare tumors. My Pediatric and Adult Rare Tumor (MyPART) network was established to engage patients, advocates, and researchers and conduct a comprehensive longitudinal Natural History Study of Rare Solid Tumors. Through remote or in-person enrollment at the NIH Clinical Center, participants with rare solid tumors ≥4 weeks old complete standardized medical and family history forms, patient reported outcomes, and provide tumor, blood and/or saliva samples. Medical records are extracted for clinical status and treatment history, and tumors undergo genomic analysis. A total of 200 participants (65% female, 35% male, median age at diagnosis 43 years, range = 2-77) enrolled from 46 U.S. states and nine other countries (46% remote, 55% in-person). Frequent diagnoses were neuroendocrine neoplasms (NEN), adrenocortical carcinomas (ACC), medullary thyroid carcinomas (MTC), succinate dehydrogenase (SDH)-deficient gastrointestinal stromal tumors (sdGIST), and chordomas. At enrollment, median years since diagnosis was 3.5 (range = 0-36.6), 63% participants had metastatic disease and 20% had no evidence of disease. Pathogenic germline and tumor mutations included SDHA/B/C (sdGIST), RET (MTC), TP53 and CTNNB1 (ACC), MEN1 (NEN), and SMARCB1 (poorly-differentiated chordoma). Clinically significant anxiety was observed in 20%-35% of adults. Enrollment of participants and comprehensive data collection were feasible. Remote enrollment was critical during the COVID-19 pandemic. Over 30 patients were enrolled with ACC, NEN, and sdGIST, allowing for clinical/genomic analyses across tumors. Longitudinal follow-up and expansion of cohorts are ongoing to advance understanding of disease course and establish external controls for interventional trials.

SIGNIFICANCE

This study demonstrates that comprehensive, tumor-agnostic data and biospecimen collection is feasible to characterize different rare tumors, and speed progress in research. The findings will be foundational to developing external controls groups for single-arm interventional trials, where randomized control trials cannot be conducted because of small patient populations.

In-depth understanding of higher-order genome architecture in orphan cancer

The human genome is intertwined, folded, condensed, and gradually constitutes the 3D architecture, thereby affecting transcription and widely involving in tumorigenesis. Incidence and mortality rates for orphan cancers increase due to poor early diagnosis and lack of effective medical treatments, which are now getting attention. In-depth understanding in tumorigenesis has fast-tracked over the last decade, however, the further role and mechanism of 3D genome organization in variant orphan tumorigenesis remains to be fully understood. We summarize for the first time that higher-order genome organization can provide novel insights into the occurrence mechanisms of orphan cancers, and discuss probable future research directions for drug development and anti-tumor therapies.

Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome

Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein-protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy.

Upregulation of long intergenic non-coding RNA LINC00326 inhibits non-small cell lung carcinoma progression by blocking Wnt/β-catenin pathway through modulating the miR-657/dickkopf WNT signaling pathway inhibitor 2 axis

BACKGROUND

Long intergenic non-coding RNA 326 (LINC00326) modulates hepatocarcinogenic lipid metabolism. However, the ability of LINC00326 to modulate the highly aggressive non-small cell lung carcinoma (NSCLC) is unknown. Here, LINC00326 in NSCLC was investigated, together with its effects on tumor malignancy and the underlying mechanisms of action.

METHODS

LINC00326 levels in tumor tissues and cell lines were measured by Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) and RNA fluorescence in situ hybridization (FISH). Proliferation and apoptosis were assessed in cell lines by Cell Counting Kit-8 (CCK-8), EdU staining assays and flow cytometry, respectively, and tumor growth was measured in mouse models. Possible microRNA targets of LINC00326 were predicted by bioinformatics and verified by RNA pull-down and immunoprecipitation and luciferase reporter assays. Western blotting was used to evaluate the expression of Wnt/β-catenin-associated proteins.

RESULTS

LINC00326 was downregulated in tumor tissues and cell lines. Knockdown of LINC00326 stimulated NSCLC cell proliferation and suppressed apoptosis in vitro, as well as enhancing xenograft tumor growth. LINC00326 sponged miR-657, and dickkopf WNT signaling pathway inhibitor 2 (DKK2) was found to be directly targeted by miR-657, with LINC00326 positively regulating its expression through sponging miR-657. The actions of LINC00326 knockdown on proliferation and apoptosis were reversed by stimulation of the miR-657/DKK2 axis. Furthermore, overexpression of miR-657 mitigated DKK2 inhibition on Wnt/β-catenin signaling.

CONCLUSIONS

LINC00326/miR-657/DKK2 axis signaling blocked tumor-associated functions in NSCLC cells through the targeting Wnt/β-catenin pathway. This suggests that this pathway could be a target for NSCLC treatment.

The impact of violating the independence assumption in meta-analysis on biomarker discovery

With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.

miR-657 Targets SRCIN1 via the Slug Pathway to Promote NSCLC Tumor Growth and EMT Induction

Background

MicroRNA- (miR-) 657 has been shown to regulate immunological and inflammatory activity, and it has also been defined to be dysregulated in both non-small-cell lung cancer (NSCLC) and hepatocellular carcinoma. The mechanistic role whereby miR-657 influences NSCLC progression, however, has yet to be clarified.

Methods

miR-657 and SRCIN1 expression levels were assessed via qPCR in the cell lines and tissues of NSCLC. Besides, correlations between the levels of miR-657 and NSCLC patient pathological characteristics were examined, and the Kaplan-Meier approach was employed for the evaluation of the prognostic utility of miR-657 in these patients. Moreover, the Pearson correlation analyses and dual-luciferase reporter assessments were used for detecting interactive relationships between miR-657 and SRCIN1. In addition, CCK-8, EdU, and Transwell assessments were employed for the appraisal of the ability of miR-657/SRCIN1 to regulate NSCLC cell proliferation and invasion. Western blotting was employed for the assessment of the levels of NSCLC cell proteins associated with the epithelial-mesenchymal transition (EMT) that were influenced by miR-657. The nude mice xenograft tumor model is established to observe the effect of miR-657 on NSCLC growth in vivo.

Results

NSCLC patient tissues and cell lines exhibited upregulated miR-657 expression that was closely related to tumor differentiation, lymphoid metastasis, and TNM stage. High levels of miR-657 were predictive of a poorer NSCLC patient prognosis, and overexpressing miR-657 resulted in the more rapid growth of NCI-H1650 and A549 cells, with a concomitant increase in their invasion. In addition, miR-657 overexpression raised the levels of Slug, N-cadherin, and Vimentin in these two cell lines while promoting E-cadherin downregulation. Dual-luciferase reporter assays confirmed that miR-657 was capable of binding to the SRCIN1 gene, and SRCIN1 expression levels were negatively associated with those of miR-657, indicating that it acts as a negative regulator of this gene. Knocking down SRCIN1 was capable to reverse the influences of miR-657 inhibitor treatment on NSCLC cell behavior. Finally, in vivo studies showed that miR-657 promoted NSCLC cell growth.

Conclusion

The obtained findings illuminate that miR-657 can promote the growth of tumors and the induction of the EMT in NSCLC cells by targeting SRCIN1 expression and modulating Slug pathway activation, highlighting this pathway as a promising therapeutic target in cases suffering from NSCLC.

KRAS Mutation in Rare Tumors: A Landscape Analysis of 3453 Chinese Patients

KRAS is the most commonly mutated oncogene in human cancers. Targeted therapy and immunotherapy for this gene have made remarkable progress in recent years. However, comprehensive molecular landscape analysis of KRAS in rare tumors is lacking. Retrospective analysis was performed on clinical samples from patients with rare tumors collected between September 2015 and September 2021, using hybrid-capture-based next-generation sequencing for genomic profiling and immunohistochemistry assay for PD-L1. Of the 3,453 patients included in analysis, KRAS mutations were identified in 8.7% patients in overall; mutation rate and mutation subtypes varied widely across tumor systems and subtypes. KRAS mutations included 21 missense mutations, of which G12D (29.2%), G12V (24.6%), and G13D (10.8%) were most common. Interestingly, KRAS G12C was observed in 0.6% patients overall, and in 5.7% of sarcomatoid carcinoma of the lung and 5.4% of clear cell ovarian cancer tumors, but none in small-bowel cancer tumors. 31.8% KRAS mutations and 36.4% KRAS G12C mutations co-occurred with other targetable alterations. No significant correlation was observed between TMB-H, MSI-H, PD-L1 status, and KRAS mutation status, which may be related to the high proportion of G12D. This study is the first KRAS mutation landscape study in rare tumors of large sample size in China and worldwide. Our results suggest that targeted therapy and immunotherapy are both feasible, albeit complex, in these patients. This information may have significant impact on the operation of clinical trials for rare tumor patients with KRAS mutations in China.

Integrative Analysis Identifies Multi-Omics Signatures That Drive Molecular Classification of Uveal Melanoma

By iCluster analysis, we found that the integrative molecular classification of the UM was primarily driven by DNA copy number variation on chromosomes 3, 6 and 8, differential methylation and expression of genes involved in the immune system, cell morphogenesis, movement and migration, and differential mutation of genes including GNA11, BAP1, EIF1AX, SF3B1 and GNAQ. Integrative analysis revealed that pathways including IL6/JAK/STAT3 signaling, angiogenesis, allograft rejection, inflammatory response and interferon gamma response were hypomethylated and up-regulated in the M3 iSubtype, which was associated with a worse overall survival, compared to the D3 iSubtype. Using two independent gene expression datasets, we demonstrated that the subtype-driving genes had an excellent prognostic power in classifying UM into high- or low-risk groups for metastasis. Integrative analysis of UM multi-omics data provided a comprehensive view of UM biology for understanding the underlying mechanism leading to UM metastasis. The concordant molecular alterations at multi-omics levels revealed by our integrative analysis could be used for patient stratification towards personalized management and surveillance.

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

Clinicopathologic profiling and oncologic outcomes of secretory carcinoma of the breast

Secretory carcinoma of the breast (SCB) is a rather rare entity of invasive breast cancer, the clinicopathologic characteristics and survival outcomes remain to be elaborated. A retrospective review was conducted in SEER database. A total of 190 SCB patients identified in SEER were eligible for inclusion in the analysis. Median age at diagnosis was 56 years (range 2–96 years). Both sexes and bilateral breast could be affected. Intriguingly, the incidence of SCB tended towards to decreasing in recent decades. Small tumor burden was observed with a mean tumor size of 2.13 cm. In a subgroup with sufficient details, positive staining of estrogen receptor (ER) and progesterone receptor (PR) was 58% and 40%, respectively. The vast majority of patients were of well to moderate differentiation (86.86%) and negative regional lymph nodes involvement (70.71%). Nearly half of the patients took radiotherapy and chemotherapy. Seniors were inclined to have an inferior breast cancer specific survival (BCSS) than their younger counterparts (P = 0.018). Patients underwent breast conserving surgery (BCS) and radiotherapy had much better BCSS than its mastectomy counterparts (P = 0.014). Collectively, SCB is a clinical indolent invasive breast cancer with excellent prognosis. BCS in conjunction with radiotherapy would be a rational alternative for this distinct entity.

Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy

Only a subgroup of patients with muscle-invasive bladder cancer (MIBC) are responders toward cisplatin-based chemotherapy and PD-L1 blockade immunotherapy. There is a clinical need to identify MIBC molecular subtypes and biomarkers for patient stratification toward the therapies. Here, we performed an integrative clustering analysis of 388 MIBC samples with multi-omics data and identified basal and luminal/differentiated integrative subtypes and derived a 42 gene panel for classification of MIBC. Using nine additional gene expression data (n = 844), we demonstrated the prognostic value of the 42 basal-luminal genes. The basal subtype was associated with worse overall survival in patients receiving no neoadjuvant chemotherapy (NAC), but better overall survival in patients receiving NAC in two clinical trials. Each of the subtypes could be further divided into chr9 p21.3 normal or loss subgroup. The patients with low expression of MTAP/CDKN2A/2B (indicative of chr9 p21.3 loss) had a significantly lower response rate to anti-PD-L1 immunotherapy and worse survival than the patients with high expression of MTAP/CDKN2A/2B. This integrative analysis reveals intrinsic MIBC subtypes and biomarkers with prognostic value for the frontline therapies.

Plant Biosystems Design Research Roadmap 1.0

Human life intimately depends on plants for food, biomaterials, health, energy, and a sustainable environment. Various plants have been genetically improved mostly through breeding, along with limited modification via genetic engineering, yet they are still not able to meet the ever-increasing needs, in terms of both quantity and quality, resulting from the rapid increase in world population and expected standards of living. A step change that may address these challenges would be to expand the potential of plants using biosystems design approaches. This represents a shift in plant science research from relatively simple trial-and-error approaches to innovative strategies based on predictive models of biological systems. Plant biosystems design seeks to accelerate plant genetic improvement using genome editing and genetic circuit engineering or create novel plant systems through de novo synthesis of plant genomes. From this perspective, we present a comprehensive roadmap of plant biosystems design covering theories, principles, and technical methods, along with potential applications in basic and applied plant biology research. We highlight current challenges, future opportunities, and research priorities, along with a framework for international collaboration, towards rapid advancement of this emerging interdisciplinary area of research. Finally, we discuss the importance of social responsibility in utilizing plant biosystems design and suggest strategies for improving public perception, trust, and acceptance.