Patient Enrichment for Precision-Based Cancer Clinical Trials: Using Prospective Cohort Surveillance as an Approach to Improve Clinical Trials

Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence

PURPOSE

Endometrial cancer (EC) is the most common gynecologic cancer in the United States with rising incidence and mortality. Despite optimal treatment, 15%-20% of all patients will recur. To better select patients for adjuvant therapy, it is important to accurately predict patients at risk for recurrence. Our objective was to train, validate, and test models of EC recurrence using lasso regression and other machine learning (ML) and deep learning (DL) analytics in a large, comprehensive data set.

METHODS

Data from patients with EC were downloaded from the Oncology Research Information Exchange Network database and stratified into low risk, The International Federation of Gynecology and Obstetrics (FIGO) grade 1 and 2, stage I (N = 329); high risk, or FIGO grade 3 or stages II, III, IV (N = 324); and nonendometrioid histology (N = 239) groups. Clinical, pathologic, genomic, and genetic data were used for the analysis. Genomic data included microRNA, long noncoding RNA, isoforms, and pseudogene expressions. Genetic variation included single-nucleotide variation (SNV) and copy-number variation (CNV). In the discovery phase, we selected variables informative for recurrence (P < .05), using univariate analyses of variance. Then, we trained, validated, and tested multivariate models using selected variables and lasso regression, MATLAB (ML), and TensorFlow (DL).

RESULTS

Recurrence clinic models for low-risk, high-risk, and high-risk nonendometrioid histology had AUCs of 56%, 70%, and 65%, respectively. For training, we selected models with AUC >80%: five for the low-risk group, 20 models for the high-risk group, and 20 for the nonendometrioid group. The two best low-risk models included clinical data and CNVs. For the high-risk group, three of the five best-performing models included pseudogene expression. For the nonendometrioid group, pseudogene expression and SNV were overrepresented in the best models.

CONCLUSION

Prediction models of EC recurrence built with ML and DL analytics had better performance than models with clinical and pathologic data alone. Prospective validation is required to determine clinical utility.

The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

SIGNIFICANCE

We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

The Tumor Microbiome Reacts to Hypoxia and Can Influence Response to Radiation Treatment in Colorectal Cancer

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiation's ability to kill cells. We hypothesized that some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. Therefore, we examined the RNA sequencing data from the Oncology Research Information Exchange Network database of patients with colorectal cancer treated with radiotherapy. We identified microbial RNAs for each tumor and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To experimentally investigate this concept, we implanted CT26 colorectal cancer cells into immune-competent BALB/c and immune-deficient athymic nude mice. After growth, in which tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed a metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-tropic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Thus, hypoxia gene expression scores seem to associate with different microbial populations and elicit an adaptive transcriptional response in intratumoral microbes, potentially influencing clinical outcomes.

SIGNIFICANCE

Tumor hypoxia reduces radiotherapy efficacy. In this study, we explored whether some of the clinical effects of hypoxia could be due to interaction with the tumor microbiome. Hypoxic gene expression scores associated with certain microbes and elicited an adaptive transcriptional response in others that could contribute to poor clinical outcomes.

Inherited Germline Variants in Urinary Tract Cancer: A Multicenter Whole-Exome Sequencing Analysis and Correlation With Clinical Features and Tumor Genomics

PURPOSE

This study investigates a real-world multicenter cohort of patients with urinary tract cancer (UTC), with primary disease sites including the bladder, urethra, and upper tract, who enrolled for research molecular testing of their germline and tumor. The purpose of this study was to evaluate factors that could affect the likelihood of identifying a clinically actionable germline pathogenic variant (PV).

METHODS

Patients with UTC were identified from 10 cancer institutes of the Oncology Research Information Exchange Network consortium. The data set comprised abstracted clinical data with germline and tumor genomic data, and comparative analyses were conducted.

RESULTS

Clinically actionable germline PVs in cancer predisposition genes were identified in 16 (4.5%) of 354 patients. A higher proportion of patients with the urethra and the upper tract as the primary sites of disease had PVs with a prevalence of 11% (5/45), compared with only 3.6% (11/308) in those with the bladder as the primary site of disease (P = .04). There were no significant differences in markers of genomic instability (such as tumor mutational burden, microsatellite instability [MSI], and loss of heterozygosity, copy number, and chromosomal instability) between those with PVs and those without (P > .05). Of the PVs identified, 10 (62%) were in homologous recombination repair (HRR) genes, three (19%) in mismatch repair (MMR) genes, and three (19%) in genes associated with other pathways.

CONCLUSION

Tissue-based assessment of genomic instability, such as MSI, does not reliably indicate germline PV. A comprehensive clinical germline testing approach that includes HRR genes in addition to MMR genes is likely to yield PVs in approximately one of 10 patients with nonbladder primary disease sites such as the upper tract and the urethra.

Clinical and Genomic Features of Patients with Renal Cell Carcinoma and Advanced Chronic Kidney Disease: Analysis of a Multi-Institutional Database

BACKGROUND

Patients with advanced chronic kidney disease (ACKD) are at an increased risk of developing renal cell carcinoma (RCC), but molecular alterations in RCC specimens arising from ACKD and overall survival (OS) in affected patients are not well defined.

PATIENTS AND METHODS

Using the Oncology Research Information Exchange Network (ORIEN) Total Cancer Care® protocol, 296 consented adult patients with RCC and somatic tumor whole exome sequencing were included. Patients with ACKD were defined as those with serum creatinine ≥1.5 mg/dL prior to RCC diagnosis.

RESULTS

Of 296 patients with RCC, 61 met the criteria for ACKD. The most common somatic mutations in the overall cohort were in VHL (126, 42.6%), PBRM1 (102, 34.5%), and SETD2 (54, 18.2%). BAP1 had a decreased mutational frequency in RCC specimens from patients without ACKD as compared to those with ACKD (10.6% versus 1.6%), but this was not statistically significant in univariable (OR 0.14, p = 0.056) or multivariable (OR 0.15, p = 0.067) analysis. Median OS was not reached in either cohort.

CONCLUSIONS

Using the clinicogenomic ORIEN database, our study found lower rates of BAP1 mutations in RCC specimens from patients with ACKD, which may reflect a BAP1-independent mutational driver of RCC in patients with ACKD.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

SIGNIFICANCE

Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool called {exotic} for "exogenous sequences in tumors and immune cells" to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches and DNA-amplicon-based measurements of a subset of the same tumors. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The {exotic} tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets.

SIGNIFICANCE

The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.

Whole-Exome sequencing analysis identified TMSB10/TRABD2A locus to be associated with carfilzomib-related cardiotoxicity among patients with multiple myeloma

Background

Proteasome inhibitor Carfilzomib (CFZ) is effective in treating patients with refractory or relapsed multiple myeloma (MM) but has been associated with cardiovascular adverse events (CVAE) such as hypertension, cardiomyopathy, and heart failure. This study aimed to investigate the contribution of germline genetic variants in protein-coding genes in CFZ-CVAE among MM patients using whole-exome sequencing (WES) analysis.

Methods

Exome-wide single-variant association analysis, gene-based analysis, and rare variant analyses were performed on 603,920 variants in 247 patients with MM who have been treated with CFZ and enrolled in the Oncology Research Information Exchange Network (ORIEN) at the Moffitt Cancer Center. Separate analyses were performed in European Americans and African Americans followed by a trans-ethnic meta-analysis.

Results

The most significant variant in the exome-wide single variant analysis was a missense variant rs7148 in the thymosin beta-10/TraB Domain Containing 2A (TMSB10/TRABD2A) locus. The effect allele of rs7148 was associated with a higher risk of CVAE [odds ratio (OR) = 9.3 with a 95% confidence interval of 3.9-22.3, p = 5.42*10^-7]. MM patients with rs7148 AG or AA genotype had a higher risk of CVAE (50%) than those with GG genotype (10%). rs7148 is an expression quantitative trait locus (eQTL) for TRABD2A and TMSB10. The gene-based analysis also showed TRABD2A as the most significant gene associated with CFZ-CVAE (p = 1.06*10^-6).

Conclusions

We identified a missense SNP rs7148 in the TMSB10/TRABD2A as associated with CFZ-CVAE in MM patients. More investigation is needed to understand the underlying mechanisms of these associations.

The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICIs). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA-seq was conducted on the formalin-fixed paraffin-embedded (FFPE) tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival ≥24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The 71 patients with metastatic melanoma ranged in age from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy responsive versus non-responsive tumors. Responders showed significant enrichment of several microbes including Fusobacterium nucleatum, and non-responders showed enrichment of fungi, as well as several bacteria. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Real-World Evaluation of Universal Germline Screening for Cancer Treatment-Relevant Pharmacogenes

The purpose of this study was to determine the frequency of clinically actionable treatment-relevant germline pharmacogenomic variants in patients with cancer and assess the real-world clinical utility of universal screening using whole-exome sequencing in this population. Cancer patients underwent research-grade germline whole-exome sequencing as a component of sequencing for somatic variants. Analysis in a clinical bioinformatics pipeline identified clinically actionable pharmacogenomic variants. Clinical Pharmacogenetics Implementation Consortium guidelines defined clinical actionability. We assessed clinical utility by reviewing electronic health records to determine the frequency of patients receiving pharmacogenomically actionable anti-cancer agents and associated outcomes. This observational study evaluated 291 patients with cancer. More than 90% carried any clinically relevant pharmacogenetic variant. At least one disease-relevant variant impacting anti-cancer agents was identified in 26.5% (77/291). Nine patients with toxicity-associated pharmacogenomic variants were treated with a relevant medication: seven UGT1A1 intermediate metabolizers were treated with irinotecan, one intermediate DPYD metabolizer was treated with 5-fluorouracil, and one TPMT poor metabolizer was treated with mercaptopurine. These individuals were more likely to experience treatment-associated toxicities than their wild-type counterparts (p = 0.0567). One UGT1A1 heterozygote died after a single dose of irinotecan due to irinotecan-related adverse effects. Identifying germline pharmacogenomic variants was feasible using whole-exome sequencing. Actionable pharmacogenetic variants are common and relevant to patients undergoing cancer treatment. Universal pharmacogenomic screening can be performed using whole-exome sequencing data originally obtained for quality control purposes and could be considered for patients who are candidates for irinotecan, 5-fluorouracil, capecitabine, and mercaptopurine.

Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

DACH1 mutation frequency in endometrial cancer is associated with high tumor mutation burden

Objective

DACH1 is a transcriptional repressor and tumor suppressor gene frequently mutated in melanoma, bladder, and prostate cancer. Loss of DACH1 expression is associated with poor prognostic features and reduced overall survival in uterine cancer. In this study, we utilized the Oncology Research Information Exchange Network (ORIEN) Avatar database to determine the frequency of DACH1 mutations in patients with endometrial cancer in our Kentucky population.

Methods

We obtained clinical and genomic data for 65 patients with endometrial cancer from the Markey Cancer Center (MCC). We examined the clinical attributes of the cancers by DACH1 status by comparing whole-exome sequencing (WES), RNA Sequencing (RNASeq), microsatellite instability (MSI), and tumor mutational burden (TMB).

Results

Kentucky women with endometrial cancer had an increased frequency of DACH1 mutations (12/65 patients, 18.5%) compared to The Cancer Genome Atlas (TCGA) endometrial cancer population (25/586 patients, 3.8%) with p-value = 1.04E-05. DACH1 mutations were associated with increased tumor mutation count in both TCGA (median 65 vs. 8972, p-value = 7.35E-09) and our Kentucky population (490 vs. 2160, p-value = 6.0E-04). DACH1 mutated patients have a higher tumor mutation burden compared to DACH1 wild-type (24 vs. 6.02, p-value = 4.29E-05). DACH1 mutations showed significant gene co-occurrence patterns with POLE, MLH1, and PMS2. DACH1 mutations were not associated with an increase in microsatellite instability at MCC (MSI-H) (p-value = 0.1342).

Conclusions

DACH1 mutations are prevalent in Kentucky patients with endometrial cancer. These mutations are associated with high tumor mutational burden and co-occur with genome destabilizing gene mutations. These findings suggest DACH1 may be a candidate biomarker for future trials with immunotherapy, particularly in endometrial cancers.

Low participation rates and disparities in participation in interventional clinical trials for myelodysplastic syndromes

BACKGROUND

The development of novel therapies for the myelodysplastic syndromes (MDS) is hampered by inadequate trial recruitment. Factors contributing to low trial accrual are incompletely understood.

METHODS

This study analyzed a pooled patient database from institutions of the US MDS Clinical Research Consortium to compare the characteristics of participants in interventional trials with those of patients who did not enroll in a trial.

RESULTS

Data were identified for 1919 patients with MDS, and 449 of these patients (23%) participated in an interventional clinical trial. The median age of all patients was 68 years, and 64% were male. Patients who participated in trials were significantly younger than nonparticipants (P = .014), and men were more likely to participate in a trial (71% of trial participants were male, whereas 61% of nonparticipants were; P < .001). Race and ethnicity were not associated with trial enrollment. Patients in more affluent ZIP codes had a higher participation rate (P < .001). Patients with intermediate- and high-risk disease according to the revised International Prognostic Scoring System were overrepresented (P = .004), and trial participants less frequently had treatment-related disease (P < .001). In multivariable analyses, participation in a clinical trial was associated with a reduced hazard of death (P = .004). Even at large referral centers, only a minority of patients with MDS enrolled in interventional trials.

CONCLUSIONS

Restrictive trial eligibility criteria that exclude patients with MDS on account of age, comorbidities, or a history of another cancer are limit enrollment of MDS patients to clinical trials. Gaining insight into the barriers to trial accrual may help investigators and study sponsors to design trials that will accrue more rapidly and augment treatment options for patients with MDS.

TENET 2.0: Identification of key transcriptional regulators and enhancers in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related death and lung adenocarcinoma is its most common subtype. Although genetic alterations have been identified as drivers in subsets of lung adenocarcinoma, they do not fully explain tumor development. Epigenetic alterations have been implicated in the pathogenesis of tumors. To identify epigenetic alterations driving lung adenocarcinoma, we used an improved version of the Tracing Enhancer Networks using Epigenetic Traits method (TENET 2.0) in primary normal lung and lung adenocarcinoma cells. We found over 32,000 enhancers that appear differentially activated between normal lung and lung adenocarcinoma. Among the identified transcriptional regulators inactivated in lung adenocarcinoma vs. normal lung, NKX2-1 was linked to a large number of silenced enhancers. Among the activated transcriptional regulators identified, CENPA, FOXM1, and MYBL2 were linked to numerous cancer-specific enhancers. High expression of CENPA, FOXM1, and MYBL2 is particularly observed in a subgroup of lung adenocarcinomas and is associated with poor patient survival. Notably, CENPA, FOXM1, and MYBL2 are also key regulators of cancer-specific enhancers in breast adenocarcinoma of the basal subtype, but they are associated with distinct sets of activated enhancers. We identified individual lung adenocarcinoma enhancers linked to CENPA, FOXM1, or MYBL2 that were associated with poor patient survival. Knockdown experiments of FOXM1 and MYBL2 suggest that these factors regulate genes involved in controlling cell cycle progression and cell division. For example, we found that expression of TK1, a potential target gene of a MYBL2-linked enhancer, is associated with poor patient survival. Identification and characterization of key transcriptional regulators and associated enhancers in lung adenocarcinoma provides important insights into the deregulation of lung adenocarcinoma epigenomes, highlighting novel potential targets for clinical intervention.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

Factors Predicting Participation in the Prospective Genomic Sequencing Study, Total Cancer Care (TCC), in Kentucky

PURPOSE

Large-scale genomic sequencing studies are driving oncology drug development. However, rural populations, like those residing in Appalachian Kentucky, are underrepresented in these efforts. In this study, we determined the frequency of participation, reasons for nonparticipation, and factors predicting the decision to participate in the Total Cancer Care (TCC) prospective genomic cohort study.

METHODS

A total of 1,188 patients were invited to enroll in the TCC prospective cohort from December 2018 to May 2019. Declining patients were queried for their rationale for nonparticipation and their patient data were obtained from the Kentucky Cancer Registry (KCR). Logistic regression was used to assess the association between characteristics and study participation. The association of study participation with survival was modeled with Cox proportional-hazards regression.

RESULTS

90.9% (1,081) patients consented to participate. In multivariate analysis, factors significantly associated with participation were age, gender, treatment status, and race. Though overall more women participated in the study, men were more likely to participate than women when invited (OR 1.57). Younger, Caucasian individuals who had received chemotherapy, but not surgery, were also more likely to participate. Patients in the Kentucky Appalachian cohort were primarily rural, had less educational attainment, and lower socioeconomic status. Kentucky Appalachian patients were no less likely to enroll in TCC than non-Appalachian patients. Consented individuals had higher overall survival compared to those who declined.

CONCLUSION

Though minorities, those with low socioeconomic status, and rural populations are underrepresented in genomic studies, they were no less likely to participate when given the opportunity, and participation was associated with better clinical outcomes.

Estimated costs of pivotal trials for U.S. Food and Drug Administration-approved cancer drugs, 2015-2017

BACKGROUND

Pivotal clinical trials provide critical evidence to regulators regarding a product's suitability for marketing approval. The objectives of this study are (1) to characterize select features of trials for oncology products approved by the U.S. Food and Drug Administration between 2015 and 2017; and (2) to quantify the costs of these trials and how such costs varied based on trial characteristics.

METHODS

We identified novel oncology therapeutic drugs, and their respective pivotal trials, approved between 2015 and 2017 using annual summary reports from the Food and Drug Administration. Cost estimates for each pivotal trial were calculated using IQVIA's CostPro, a clinical trial cost estimating tool based on executed contracts between pharmaceutical manufacturers and contract research organizations. Measures of drug and trial characteristics included trial design, end point, patient enrollment, and regulatory pathway. We also performed sensitivity analyses that varied assumptions regarding how efficiently each trial was conducted.

RESULTS

A total of 39 pivotal clinical trials provided the basis for Food and Drug Administration approval of 30 new oncology drugs from 2015 to 2017. Among these trials, primary end points were objective response rate in 20 (51.3%), progression-free survival in 13 (33.3%), and overall survival in 6 (15.4%). Twenty trials (51.3%) were single-arm studies. The median estimated cost of oncology pivotal trials was $31.7 million (interquartile range = $17.0-$60.4 million). Trials with objective response rate as primary end point had a median estimate of $17.7 million (interquartile range = $11.9-$27.1 million), compared with trials examining progression-free survival ($42.3 million, interquartile range = $34.6-$101.2 million) or overall survival ($79.4 million, interquartile range = $56.9-$97.7 million) (p < 0.001). Estimated costs for single-arm trials ($17.7 million, interquartile range = $11.9-$23.7 million) were less than for trials with a placebo-controlled ($56.7 million, interquartile range = $40.9-$103.9 million) or active control arm ($67.6 million, IQR = $35.5-$93.5 million) (p < 0.001).

CONCLUSIONS

Relative to the estimated costs of drug development, the costs of these oncology pivotal trials were modest, with trials that produced more valuable scientific information costing more than their counterparts.

RNA Interference-Based Cancer Drugs: The Roadblocks, and the "Delivery" of the Promise

Nucleic acid-based therapeutics like synthetic small interfering RNAs have been exploited to modulate gene function, taking advantage of RNA interference (RNAi), an evolutionally conserved biological process. Recently, the world's first RNAi drug was approved for a rare genetic disorder in the liver. However, there are significant challenges that need to be resolved before RNAi can be translated in other genetic diseases like cancer. Current drug delivery platforms for therapeutic silencing RNAs are tailored to hepatic targets. RNAi therapies for nonhepatic conditions are still at early clinical phases. In this study, we discuss the critical design considerations in anticancer RNAi drug development, insights gained from initial clinical trials, and new strategies that are entering clinical development, shaping the future of RNAi in cancer.