Craniofacial morphogenesis demands coordinated outgrowth of spatially‐separated facial prominences. Underscoring this complex choreography, 1/3 of all human birth defects affect craniofacial development. Both orofacial clefting and malocclusions are common birth abnormalities that affect the midface. During midfacial development, regulatory genes control the dynamics of transcriptional activity, tissue patterning, and intercellular signaling to modulate the timing of morphogenetic and growth processes. Variation in these dynamics underlies both evolutionary divergence in midfacial outgrowth and the etiology of craniofacial birth defects. In non‐avians, the main variable driving species‐specific midfacial length is the maxilla of the upper jaw, while in avians it is the premaxilla. Also, avians have cleft palate in physiological conditions. Despite the availability of datasets of regulatory elements from craniofacial tissues and cranial neural crest cells, the majority of regulatory networks that control embryonic midfacial outgrowth are unknown with respect to their in vivo spatiotemporal context‐dependence. We are establishing regulatory landscapes that control different morphogenetic and outgrowth characteristics of midfacial elements at the organismal level in chick, mouse, and pig embryos, species characterized by remarkably different midfacial lengths. To this end, we are combining 3D gene expression profiles obtained via High Resolution Episcopic Microscopy (HREM) of embryos processed by whole mount in situ hybridization as well as genome‐wide transcriptional regulatory activity and epigenetic regulation assessed by RNA Seq, ATAC‐Seq, and chromatin mark ChIP‐Seq of embryonic midfacial anlagen. By this approach, we have identified candidate genes that regulate maxillary‐specific morphogenetic programs in avians versus mammals. One of these genes, which encodes a transcription factor (TF) bearing zinc finger motifs and a homeobox motif, is expressed meristically along the AP palatal shelf of mouse embryos at E11.5–14.5, but is not expressed in the palatal shelves of avian embryos at the corresponding developmental stages. Mice we generated with CRISPR/Cas‐mediated loss‐of‐function of this TF show cleft palate and stunted snout. Strikingly, children with a deletion of this TF exhibit high‐arched palate and cleft palate. These findings establish mechanisms underlying tolerance to morphological variation that separate normal development and evolution of craniofacial features from malformations associated with birth defects. Support or Funding Information: UCSF Recruitment Funds

Embryonal rhabdomyosarcoma (eRMS) is one of the most common soft tissue sarcomas in children and adolescents. Parameningeal eRMS is a variant that is often more difficult to treat than eRMS occurring at other sites. A 14-year-old female with persistent headaches and rapid weight loss was diagnosed with parameningeal eRMS. She progressed and died despite chemotherapy with vincristine, actinomycin-D, and cyclophosphamide plus 50.4 Gy radiation therapy to the primary tumor site. Tumor specimens were acquired by rapid autopsy and tumor tissue was transplanted into immunodeficient mice to create a patient-derived xenograft (PDX) animal model. As autopsy specimens had an ALK R1181C mutation, PDX tumor bearing animals were treated with the pan-kinase inhibitor lestaurtinib but demonstrated no decrease in tumor growth, suggesting that single agent kinase inhibitor therapy may be insufficient in similar cases. This unique parameningeal eRMS PDX model is publicly available for preclinical study.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Background

The overarching goal of this project is to establish a patient-derived bladder cancer xenograft (PDX) platform, annotated with deep sequencing and patient clinical information, to accelerate the development of new treatment options for bladder cancer patients. Herein, we describe the creation, initial characterization and use of the platform for this purpose.

Methods and findings

Twenty-two PDXs with annotated clinical information were established from uncultured unselected clinical bladder cancer specimens in immunodeficient NSG mice. The morphological fidelity was maintained in PDXs. Whole exome sequencing revealed that PDXs and parental patient cancers shared 92-97% of genetic aberrations, including multiple druggable targets. For drug repurposing, an EGFR/HER2 dual inhibitor lapatinib was effective in PDX BL0440 (progression-free survival or PFS of 25.4 days versus 18.4 days in the control, p = 0.007), but not in PDX BL0269 (12 days versus 13 days in the control, p = 0.16) although both expressed HER2. To screen for the most effective MTT, we evaluated three drugs (lapatinib, ponatinib, and BEZ235) matched with aberrations in PDX BL0269; but only a PIK3CA inhibitor BEZ235 was effective (p<0.0001). To study the mechanisms of secondary resistance, a fibroblast growth factor receptor 3 inhibitor BGJ398 prolonged PFS of PDX BL0293 from 9.5 days of the control to 18.5 days (p<0.0001), and serial biopsies revealed that the MAPK/ERK and PIK3CA-AKT pathways were activated upon resistance. Inhibition of these pathways significantly prolonged PFS from 12 day of the control to 22 days (p = 0.001). To screen for effective chemotherapeutic drugs, four of the first six PDXs were sensitive to the cisplatin/gemcitabine combination, and chemoresistance to one drug could be overcome by the other drug.

Conclusion

The PDX models described here show good correlation with the patient at the genomic level and known patient response to treatment. This supports further evaluation of the PDXs for their ability to accurately predict a patient's response to new targeted and combination strategies for bladder cancer.

New approaches to optimization of cancer drug development in the laboratory and the clinic will be required to fully achieve the goal of individualized, precision cancer therapy. Improved preclinical models that more closely reflect the now recognized genomic complexity of human cancers are needed. Here we describe a collaborative research project that integrates core resources of The Jackson Laboratory Basic Science Cancer Center with genomics and clinical research facilities at the UC Davis Comprehensive Cancer Center to establish a clinically and genomically annotated patient-derived xenograft (PDX) platform designed to enhance new drug development and strategies for targeted therapies. Advanced stage non-small-cell lung cancer (NSCLC) was selected for initial studies because of emergence of a number of "druggable" molecular targets, and recent recognition of substantial inter- and intrapatient tumor heterogeneity. Additionally, clonal evolution after targeted therapy interventions make this tumor type ideal for investigation of this platform. Using the immunodeficient NOD scid gamma mouse, > 200 NSCLC tumor biopsies have been xenotransplanted. During the annotation process, patient tumors and subsequent PDXs are compared at multiple levels, including histomorphology, clinically applicable molecular biomarkers, global gene expression patterns, gene copy number variations, and DNA/chromosomal alterations. NSCLC PDXs are grouped into panels of interest according to oncogene subtype and/or histologic subtype. Multiregimen drug testing, paired with next-generation sequencing before and after therapy and timed tumor pharmacodynamics enables determination of efficacy, signaling pathway alterations, and mechanisms of sensitivity-resistance in individual models. This approach should facilitate derivation of new therapeutic strategies and the transition to individualized therapy.

Sarcomas are a rare subgroup of pediatric cancers comprised of a variety of bone and soft-tissue tumors. While significant advances have been made in improving outcomes of patients with localized pediatric sarcomas since the addition of systemic chemotherapy to local control many decades ago, outcomes for patients with metastatic and relapsed sarcoma remain poor with few novel therapeutics identified to date. With the advent of new technologies to study cancer genomes, transcriptomes and epigenomes, our understanding of sarcoma biology has improved tremendously in a relatively short period of time. However, much remains to be accomplished in this arena especially with regard to translating all of this new knowledge to the bedside. To this end, a meeting was convened in Philadelphia, PA, on April 18, 2015 sponsored by the QuadW foundation, Children's Oncology Group and CureSearch for Children's Cancer that brought together sarcoma clinicians and scientists from North America to review the current state of pediatric sarcoma biology and ongoing/planned genomics based clinical trials in an effort to identify and bridge knowledge gaps that continue to exist at present. At the conclusion of the workshop, three key objectives that would significantly further our understanding of sarcoma were identified and a proposal was put forward to develop an all-encompassing pediatric sarcoma biology protocol that would address these specific needs. This review summarizes the proceedings of the workshop.

Background

Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

Methods

Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.

Results

Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).

Conclusions

These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Congenital diaphragmatic hernia (CDH) is a relatively common and genetically heterogeneous structural birth defect associated with high mortality and morbidity. We describe eight unrelated families with an X-linked condition characterized by diaphragm defects, variable anterior body-wall anomalies, and/or facial dysmorphism. Using linkage analysis and exome or genome sequencing, we found that missense variants in plastin 3 (PLS3), a gene encoding an actin bundling protein, co-segregate with disease in all families. Loss-of-function variants in PLS3 have been previously associated with X-linked osteoporosis (MIM: 300910), so we used in silico protein modeling and a mouse model to address these seemingly disparate clinical phenotypes. The missense variants in individuals with CDH are located within the actin-binding domains of the protein but are not predicted to affect protein structure, whereas the variants in individuals with osteoporosis are predicted to result in loss of function. A mouse knockin model of a variant identified in one of the CDH-affected families, c.1497G>C (p.Trp499Cys), shows partial perinatal lethality and recapitulates the key findings of the human phenotype, including diaphragm and abdominal-wall defects. Both the mouse model and one adult human male with a CDH-associated PLS3 variant were observed to have increased rather than decreased bone mineral density. Together, these clinical and functional data in humans and mice reveal that specific missense variants affecting the actin-binding domains of PLS3 might have a gain-of-function effect and cause a Mendelian congenital disorder.

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.

Significance

Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

A selective sweep is the result of strong positive selection driving newly occurring or standing genetic variants to fixation, and can dramatically alter the pattern and distribution of allelic diversity in a population. Population-level sequencing data have enabled discoveries of selective sweeps associated with genes involved in recent adaptations in many species. In contrast, much debate but little evidence addresses whether selfish genes are capable of fixation-thereby leaving signatures identical to classical selective sweeps-despite being neutral or deleterious to organismal fitness. We previously described R2d2, a large copy-number variant that causes nonrandom segregation of mouse Chromosome 2 in females due to meiotic drive. Here we show population-genetic data consistent with a selfish sweep driven by alleles of R2d2 with high copy number (R2d2(HC)) in natural populations. We replicate this finding in multiple closed breeding populations from six outbred backgrounds segregating for R2d2 alleles. We find that R2d2(HC) rapidly increases in frequency, and in most cases becomes fixed in significantly fewer generations than can be explained by genetic drift. R2d2(HC) is also associated with significantly reduced litter sizes in heterozygous mothers, making it a true selfish allele. Our data provide direct evidence of populations actively undergoing selfish sweeps, and demonstrate that meiotic drive can rapidly alter the genomic landscape in favor of mutations with neutral or even negative effects on overall Darwinian fitness. Further study will reveal the incidence of selfish sweeps, and will elucidate the relative contributions of selfish genes, adaptation and genetic drift to evolution.