A utilization distribution for the global population of Cape Vultures (Gyps coprotheres) to guide wind energy development.

The rapid development of wind energy in southern Africa represents an additional threat to the already fragile populations of African vultures. The distribution of the vulnerable Cape Vulture Gyps coprotheres overlaps considerably with wind energy development areas in South Africa, creating conflicts that can hinder both vulture conservation and sustainable energy development. To help address this conflict and aid in the safe placement of wind energy facilities, we map the utilization distribution (UD) of this species across its distributional range. Using tracking data from 68 Cape Vultures collected over the last 20 years, we develop a spatially explicit habitat use model to estimate the expected UDs around known colonies. Scaling the UDs by the number of vultures expected to use each of the colonies, we estimate the Cape Vulture population utilization distribution (PUD) and determine its exposure to wind farm impacts. To complement our results, we model the probability of a vulture flying within the rotor sweep area of a wind turbine throughout the species range and use this to identify areas that are particularly prone to collisions. Overall, our estimated PUD correlates well with reporting rates of the species from the Southern African Bird Atlas Project, currently used to assess potential overlap between Cape Vultures and wind energy developments, but it adds important benefits, such as providing a spatial gradient of activity estimates over the entire species range. We illustrate the application of our maps by analyzing the exposure of Cape Vultures in the Renewable Energy Development Zones (REDZs) in South Africa. This application is a scalable procedure that can be applied at different planning phases, from strategic, nationwide planning to project-level assessments.

Correction: A feasibility study of returning clinically actionable somatic genomic alterations identified in a research laboratory.

[This corrects the article DOI: 10.18632/oncotarget.16018.].

Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.

Precision medicine focuses on DNA abnormalities, but not all tumors have tractable genomic alterations. The WINTHER trial ( NCT01856296 ) navigated patients to therapy on the basis of fresh biopsy-derived DNA sequencing (arm A; 236 gene panel) or RNA expression (arm B; comparing tumor to normal). The clinical management committee (investigators from five countries) recommended therapies, prioritizing genomic matches; physicians determined the therapy given. Matching scores were calculated post-hoc for each patient, according to drugs received: for DNA, the number of alterations matched divided by the total alteration number; for RNA, expression-matched drug ranks. Overall, 303 patients consented; 107 (35%; 69 in arm A and 38 in arm B) were evaluable for therapy. The median number of previous therapies was three. The most common diagnoses were colon, head and neck, and lung cancers. Among the 107 patients, the rate of stable disease ≥6 months and partial or complete response was 26.2% (arm A: 23.2%; arm B: 31.6% (P = 0.37)). The patient proportion with WINTHER versus previous therapy progression-free survival ratio of >1.5 was 22.4%, which did not meet the pre-specified primary end point. Fewer previous therapies, better performance status and higher matching score correlated with longer progression-free survival (all P < 0.05, multivariate). Our study shows that genomic and transcriptomic profiling are both useful for improving therapy recommendations and patient outcome, and expands personalized cancer treatment.

Cooperative Effect of Oncogenic MET and PIK3CA in an HGF-Dominant Environment in Breast Cancer.

There is compelling evidence that oncogenic MET and PIK3CA signaling pathways contribute to breast cancer. However, the activity of pharmacologic targeting of either pathway is modest. Mechanisms of resistance to these monotherapies have not been clarified. Currently, commonly used mouse models are inadequate for studying the HGF-MET axis because mouse HGF does not bind human MET. We established human HGF-MET paired mouse models. In this study, we evaluated the cooperative effects of MET and PIK3CA in an environment with involvement of human HGF in vivo Oncogenic MET/PIK3CA synergistically induced aggressive behavior and resistance to each targeted therapy in an HGF-paracrine environment. Combined targeting of MET and PI3K abrogates resistance. Associated cell signaling changes were explored by functional proteomics. Consistently, combined targeting of MET and PI3K inhibited activation of associated oncogenic pathways. We also evaluated the response of tumor cells to HGF stimulation using breast cancer patient-derived xenografts (PDX). HGF stimulation induced significant phosphorylation of MET for all PDX lines detected to varying degrees. However, the levels of phosphorylated MET are not correlated with its expression, suggesting that MET expression level cannot be used as a sole criterion to recruit patients to clinical trials for MET-targeted therapy. Altogether, our data suggest that combined targeting of MET and PI3K could be a potential clinical strategy for breast cancer patients, where phosphorylated MET and PIK3CA mutation status would be biomarkers for selecting patients who are most likely to derive benefit from these cotargeted therapy.

Survival Outcomes by TP53 Mutation Status in Metastatic Breast Cancer.

PURPOSE: We sought to determine the significant genomic alterations in patients with metastatic breast cancer (MBC), and survival outcomes in common genotypes. PATIENTS AND METHODS: High-depth next generation sequencing was performed for 202 genes in tumor and normal DNA from 257 patients with MBC, including 165 patients with ER/PR+ HER2- (hormone receptor positive, HR+ positive), 32 patients with HER2+ and 60 patients with triple negative (ER/PR/HER2-) cancer. Kaplan Meier survival analysis was performed in our discovery set, in breast cancer patients analyzed in The Cancer Genome Atlas, and in a separate cohort of 98 patients with MBC who underwent clinical genomic testing. RESULTS: Significantly mutated genes (SMGs) varied by histology and tumor subtype, but TP53 was a SMG in all three subtypes. The most SMGs in HR+ patients included PIK3CA (32%), TP53 (29%), GATA3 (15%), CDH1 (8%), MAP3K1 (8%), PTEN (5%), TGFBR2 (4%), AKT1 (4%), and MAP2K4 (4%). TP53 mutations were associated with shorter recurrence-free survival (P=0.004), progression-free survival (P=0.00057) and overall survival (P=0.003). Further, TP53 status was prognostic among HR+ patients with PIK3CA mutations. TP53 mutations were also associated with poorer overall survival in the 442 HR+ breast cancer patients in the TCGA (P=0.042) and in an independent set of 96 HR+ MBC who underwent clinical sequencing (P=0.0004). CONCLUSIONS: SMGs differ by tumor subtype but TP53 is significantly mutated in all three breast cancer subtypes. TP53 mutations are associated with poor prognosis in HR+ breast cancer. TP53 mutations should be considered in the design and interpretation of precision oncology trials.

Precision Oncology Decision Support: Current Approaches and Strategies for the Future.

With the increasing availability of genomics, routine analysis of advanced cancers is now feasible. Treatment selection is frequently guided by the molecular characteristics of a patient's tumor, and an increasing number of trials are genomically selected. Furthermore, multiple studies have demonstrated the benefit of therapies that are chosen based upon the molecular profile of a tumor. However, the rapid evolution of genomic testing platforms and emergence of new technologies make interpreting molecular testing reports more challenging. More sophisticated precision oncology decision support services are essential. This review outlines existing tools available for health care providers and precision oncology teams and highlights strategies for optimizing decision support. Specific attention is given to the assays currently available for molecular testing, as well as considerations for interpreting alteration information. This article also discusses strategies for identifying and matching patients to clinical trials, current challenges, and proposals for future development of precision oncology decision support. Clin Cancer Res; 24(12); 2719-31. ©2018 AACR.

Physician interpretation of genomic test results and treatment selection.

BACKGROUND: Genomic testing is increasingly performed in oncology, but concerns remain regarding the clinician's ability to interpret results. In the current study, the authors sought to determine the agreement between physicians and genomic annotators from the Precision Oncology Decision Support (PODS) team at The University of Texas MD Anderson Cancer Center in Houston regarding actionability and the clinical use of test results. METHODS: On a prospective protocol, patients underwent clinical genomic testing for hotspot mutations in 46 or 50 genes. Six months after sequencing, physicians received questionnaires for patients who demonstrated a variant in an actionable gene, investigating their perceptions regarding the actionability of alterations and clinical use of these findings. Genomic annotators independently classified these variants as actionable, potentially actionable, unknown, or not actionable. RESULTS: Physicians completed 250 of 288 questionnaires (87% response rate). Physicians considered 168 of 250 patients (67%) as having an actionable alteration; of these, 165 patients (98%) were considered to have an actionable alteration by the PODS team and 3 were of unknown significance. Physicians were aware of genotype-matched therapy available for 119 patients (71%) and 48 of these 119 patients (40%) received matched therapy. Approximately 46% of patients in whom physicians regarded alterations as not actionable (36 of 79 patients) were classified as having an actionable/potentially actionable mutation by the PODS team. However, many of these were only theoretically actionable due to limited trials and/or therapies (eg, KRAS). CONCLUSIONS: Physicians are aware of recurrent mutations in actionable genes on "hotspot" panels. As larger genomic panels are used, there may be a growing need for annotation of actionability. Decision support to increase awareness of genomically relevant trials and novel treatment options for recurrent mutations (eg, KRAS) also are needed. Cancer 2018;124:966-72. © 2017 American Cancer Society.

Rare, Intense, Big fires dominate the global tropics under drier conditions.

Wildfires burn large parts of the tropics every year, shaping ecosystem structure and functioning. Yet the complex interplay between climate, vegetation and human factors that drives fire dynamics is still poorly understood. Here we show that on all continents, except Australia, tropical fire regimes change drastically as mean annual precipitation falls below 550 mm. While the frequency of fires decreases below this threshold, the size and intensity of wildfires rise sharply. This transition to a regime of Rare-Intense-Big fires (RIB-fires) corresponds to the relative disappearance of trees from the landscape. Most dry regions on the globe are projected to become substantially drier under global warming. Our findings suggest a global zone where this drying may have important implications for fire risks to society and ecosystem functioning.

"Personalized Cancer Therapy": A Publicly Available Precision Oncology Resource.

High-throughput genomic and molecular profiling of tumors is emerging as an important clinical approach. Molecular profiling is increasingly being used to guide cancer patient care, especially in advanced and incurable cancers. However, navigating the scientific literature to make evidence-based clinical decisions based on molecular profiling results is overwhelming for many oncology clinicians and researchers. The Personalized Cancer Therapy website (www.personalizedcancertherapy.org) was created to provide an online resource for clinicians and researchers to facilitate navigation of available data. Specifically, this resource can be used to help identify potential therapy options for patients harboring oncogenic genomic alterations. Herein, we describe how content on www.personalizedcancertherapy.org is generated and maintained. We end with case scenarios to illustrate the clinical utility of the website. The goal of this publicly available resource is to provide easily accessible information to a broad oncology audience, as this may help ease the information retrieval burden facing participants in the precision oncology field. Cancer Res; 77(21); e123-6. ©2017 AACR.

Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study.

PURPOSE: Genomic profiling is increasingly used in the management of cancer. We have previously reported preliminary results of our precision medicine program. Here, we present response and survival outcomes for 637 additional patients who were referred for phase I trials and were treated with matched targeted therapy (MTT) when available. PATIENTS AND METHODS: Patients with advanced cancer who underwent tumor genomic analyses were treated with MTT when available. RESULTS: Overall, 1,179 (82.1%) of 1,436 patients had one or more alterations (median age, 59.7 years; men, 41.2%); 637 had one or more actionable aberrations and were treated with MTT (n = 390) or non-MTT (n = 247). Patients who were treated with MTT had higher rates of complete and partial response (11% v 5%; P = .0099), longer failure-free survival (FFS; 3.4 v 2.9 months; P = .0015), and longer overall survival (OS; 8.4 v 7.3 months; P = .041) than did unmatched patients. Two-month landmark analyses showed that, for MTT patients, FFS for responders versus nonresponders was 7.6 versus 4.3 months (P < .001) and OS was 23.4 versus 8.5 months (P < .001), whereas for non-MTT patients (responders v nonresponders), FFS was 6.6 versus 4.1 months (P = .001) and OS was 15.2 versus 7.5 months (P = .43). Patients with phosphatidylinositol 3-kinase (PI3K) and mitogen-activated protein kinase pathway alterations matched to PI3K/Akt/mammalian target of rapamycin axis inhibitors alone demonstrated outcomes comparable to unmatched patients. CONCLUSION: Our results support the use of genomic matching. Subset analyses indicate that matching patients who harbor a PI3K and mitogen-activated protein kinase pathway alteration to only a PI3K pathway inhibitor does not improve outcome. We have initiated IMPACT2, a randomized trial to compare treatment with and without genomic selection.

A feasibility study of returning clinically actionable somatic genomic alterations identified in a research laboratory.

PURPOSE: Molecular profiling performed in the research setting usually does not benefit the patients that donate their tissues. Through a prospective protocol, we sought to determine the feasibility and utility of performing broad genomic testing in the research laboratory for discovery, and the utility of giving treating physicians access to research data, with the option of validating actionable alterations in the CLIA environment. EXPERIMENTAL DESIGN: 1200 patients with advanced cancer underwent characterization of their tumors with high depth hybrid capture sequencing of 201 genes in the research setting. Tumors were also tested in the CLIA laboratory, with a standardized hotspot mutation analysis on an 11, 46 or 50 gene platform. RESULTS: 527 patients (44%) had at least one likely somatic mutation detected in an actionable gene using hotspot testing. With the 201 gene panel, 945 patients (79%) had at least one alteration in a potentially actionable gene that was undetected with the more limited CLIA panel testing. Sixty-four genomic alterations identified on the research panel were subsequently tested using an orthogonal CLIA assay. Of 16 mutations tested in the CLIA environment, 12 (75%) were confirmed. Twenty-five (52%) of 48 copy number alterations were confirmed. Nine (26.5%) of 34 patients with confirmed results received genotype-matched therapy. Seven of these patients were enrolled onto genotype-matched targeted therapy trials. CONCLUSION: Expanded cancer gene sequencing identifies more actionable genomic alterations. The option of CLIA validating research results can provide alternative targets for personalized cancer therapy.

ALK: a tyrosine kinase target for cancer therapy.

The anaplastic lymphoma kinase (ALK) gene plays an important physiologic role in the development of the brain and can be oncogenically altered in several malignancies, including non-small-cell lung cancer (NSCLC) and anaplastic large cell lymphomas (ALCL). Most prevalent ALK alterations are chromosomal rearrangements resulting in fusion genes, as seen in ALCL and NSCLC. In other tumors, ALK copy-number gains and activating ALK mutations have been described. Dramatic and often prolonged responses are seen in patients with ALK alterations when treated with ALK inhibitors. Three of these-crizotinib, ceritinib, and alectinib-are now FDA approved for the treatment of metastatic NSCLC positive for ALK fusions. However, the emergence of resistance is universal. Newer ALK inhibitors and other targeting strategies are being developed to counteract the newly emergent mechanism(s) of ALK inhibitor resistance. This review outlines the recent developments in our understanding and treatment of tumors with ALK alterations.

Clinical Use of Precision Oncology Decision Support.

PURPOSE: Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support (PODS) team that provides annotations at the alteration-level and subsequently determined if clinical decision-making was influenced. METHODS: Genomic alterations were annotated to determine actionability based on a variant's known or potential functional and/or therapeutic significance. The medical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A web-based survey was implemented to capture the reasons why genotype-matched therapies were not pursued. RESULTS: PODS processed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially, 29.7% unknown, 28.4% non-actionable. 66% of patients had at least one actionable/potentially actionable alteration. 20.6% (110/535) patients annotated enrolled on a genotype-matched trial. Trial enrolment was significantly higher for patients with actionable/potentially actionable alterations (92/333, 27.6%) than those with unknown (16/136, 11.8%) and non-actionable (2/66, 3%) alterations (p=0.00004). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment on genotype-matched trials. Clinicians cited a variety of reasons why patients with actionable alterations did not enroll on trials. CONCLUSION: Over half of alterations annotated were of unknown significance or non-actionable. Physicians were more likely to enroll a patient on a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.

Clinical outcomes based on multigene profiling in metastatic breast cancer patients.

BACKGROUND: Identifying the clinical impact of recurrent mutations can help define their role in cancer. Here, we identify frequent hotspot mutations in metastatic breast cancer (MBC) patients and associate them with clinical outcomes. PATIENTS AND METHODS: Hotspot mutation testing was conducted in 500 MBC patients using an 11 gene (N = 126) and/or 46 or 50 gene (N = 391) panel. Patients were stratified by hormone receptor (HR) and human epidermal growth factor 2 (HER2) status. Clinical outcomes were retrospectively collected. RESULTS: Hotspot mutations were most frequently detected in TP53 (30%), PIK3CA (27%) and AKT1 (4%). Triple-negative breast cancer (TNBC) patients had the highest incidence of TP53 (58%) and the lowest incidence of PIK3CA (9%) mutations. TP53 mutation was associated with shorter relapse-free survival (RFS) (median 22 vs 42months; P < 0.001) and overall survival (OS) from diagnosis of distant metastatic disease (median 26 vs 51months; P < 0.001). Conversely, PIK3CA mutation was associated with a trend towards better clinical outcomes including RFS (median 41 vs 30months; P = 0.074) and OS (52 vs 40months; P = 0.066). In HR-positive patients, TP53 mutation was again associated with shorter RFS (median 30 vs 46months; P = 0.017) and OS (median 30 vs 55months; P = 0.001). When multivariable analysis was performed for RFS and OS, TP53 but not PIK3CA mutation remained a significant predictor of outcomes in the overall cohort and in HR-positive patients. CONCLUSIONS: Clinical hotspot sequencing identifies potentially actionable mutations. In this cohort, TP53 mutation was associated with worse clinical outcomes, while PIK3CA mutation did not remain a significant predictor of outcomes after multivariable analysis.

Prevalence of actionable mutations and copy number alterations and the price of a genomic testing panel.

Interest in genomic testing for the selection of cancer therapy is growing. However, the cost of genomic testing has not been well studied. We sought to determine the price of identifying mutations and copy number alterations (CNAs) in theoretically actionable genes across multiple tumor types. We reviewed data from The Cancer Genome Atlas to determine the frequency of alterations in nine tumor types. We used price information from a commonly used commercial genomic testing platform (FoundationOne) to determine the price of detecting mutations and CNAs in different types of tumors. Although there are large variations in the prevalence by tumor type, when the detection of both mutations and CNAs was considered overall, most patients had at least one alteration in a potentially actionable gene (84% overall, range 51%- 98% among tumor types assessed). The corresponding average price of identifying at least one alteration per patient ranges from $5,897 to $11,572. Although the frequency of mutations and CNAs in actionable genes differs by tumor type, most patients have an actionable genomic alteration detectable by a commercially available panel. Determining CNAs as well as mutations improves actionability and reduces the price of detecting an alteration.

Association of Biomarker-Based Treatment Strategies With Response Rates and Progression-Free Survival in Refractory Malignant Neoplasms: A Meta-analysis.

IMPORTANCE: The impact of a biomarker-based (personalized) cancer treatment strategy in the setting of phase 1 clinical trials was analyzed. OBJECTIVE: To compare patient outcomes in phase 1 studies that used a biomarker selection strategy with those that did not. DATA SOURCES: PubMed search of phase 1 cancer drug trials (January 1, 2011, through December 31, 2013). STUDY SELECTION: Studies included trials that evaluated single agents, and reported efficacy end points (at least response rate [RR]). DATA EXTRACTION AND SYNTHESIS: Data were extracted independently by 2 investigators. MAIN OUTCOMES AND MEASURES: Response rate and progression-free survival (PFS) were compared for arms that used a personalized strategy (biomarker selection) vs those that did not. Overall survival was not analyzed owing to insufficient data. RESULTS: A total of 346 studies published in the designated 3-year time period were included in the analysis. Multivariable analysis (meta-regression and weighted multiple regression models) demonstrated that the personalized approach independently correlated with a significantly higher median RR (30.6% [95% CI, 25.0%-36.9%] vs 4.9% [95% CI, 4.2%-5.7%]; P < .001) and a longer median PFS (5.7 [95% CI, 2.6-13.8] vs 2.95 [95% CI, 2.3-3.7] months; P < .001). Targeted therapy arms that used a biomarker-based selection strategy (n = 57 trials) were associated with statistically improved RR compared with targeted therapy arms (n = 177 arms) that did not (31.1% [95% CI, 25.4%-37.4%] vs 5.1% [95% CI, 4.3%-6.0%]; P < .001). Nonpersonalized targeted arms had outcomes comparable with those that tested a cytotoxic agent (median RR, 5.1% [95% CI, 4.3%-6.0%] vs 4.7% [95% CI, 3.6%-6.2%]; P = .63; respectively; median PFS, 3.3 [95% CI, 2.6-4.0] months vs 2.5 [95% CI, 2.0-3.7] months; P = .22). Personalized arms using a "genomic (DNA) biomarker" had higher median RR than those using a "protein biomarker" (42.0% [95% CI, 33.7%-50.9%] vs 22.4% [95% CI, 15.6%-30.9%]; P = .001). The median treatment-related mortality was not statistically different for arms that used a personalized strategy vs not (1.89% [95% CI, 1.36%-2.61%] vs 2.27% [95% CI, 1.97%-2.62%]; P = .31). CONCLUSIONS AND RELEVANCE: In this meta-analysis, most phase 1 trials of targeted agents did not use a biomarker-based selection strategy. However, use of a biomarker-based approach was associated with significantly improved outcomes (RR and PFS). Response rates were significantly higher with genomic vs protein biomarkers. Studies that used targeted agents without a biomarker had negligible response rates.