Truncated FGFR2 is a clinically actionable oncogene in multiple cancers.

Somatic hotspot mutations and structural amplifications and fusions that affect fibroblast growth factor receptor 2 (encoded by FGFR2) occur in multiple types of cancer1. However, clinical responses to FGFR inhibitors have remained variable1-9, emphasizing the need to better understand which FGFR2 alterations are oncogenic and therapeutically targetable. Here we apply transposon-based screening10,11 and tumour modelling in mice12,13, and find that the truncation of exon 18 (E18) of Fgfr2 is a potent driver mutation. Human oncogenomic datasets revealed a diverse set of FGFR2 alterations, including rearrangements, E1-E17 partial amplifications, and E18 nonsense and frameshift mutations, each causing the transcription of E18-truncated FGFR2 (FGFR2ΔE18). Functional in vitro and in vivo examination of a compendium of FGFR2ΔE18 and full-length variants pinpointed FGFR2-E18 truncation as single-driver alteration in cancer. By contrast, the oncogenic competence of FGFR2 full-length amplifications depended on a distinct landscape of cooperating driver genes. This suggests that genomic alterations that generate stable FGFR2ΔE18 variants are actionable therapeutic targets, which we confirmed in preclinical mouse and human tumour models, and in a clinical trial. We propose that cancers containing any FGFR2 variant with a truncated E18 should be considered for FGFR-targeted therapies.

Targetable alterations in invasive pleomorphic lobular carcinoma of the breast.

BACKGROUND: Invasive pleomorphic lobular carcinoma (PLC) of the breast is a subtype of invasive lobular cancer which compromises approximately 1% of all epithelial breast malignancies and is characterized by higher nuclear pleomorphism and poorer prognosis than classic invasive lobular cancer (ILC). Since PLC is more aggressive than classical ILC, we examined the underlying molecular alterations in this subtype of breast cancer to understand the possible benefit from targeted therapies. METHODS: In this study, we investigate the clinical characteristics and molecular alterations in 16 PLC from our institution. Additionally, we examined the clinical and genomic features in 31 PLC from the Cancer Genome Atlas (TCGA). RESULTS: Overall, our analysis of PLC found that 28% had activating ERBB2 mutations, 21% had ERBB2 amplification, and 49% activating PIK3CA mutations. Among cases from our institution, we found 19% with activating ERBB2 mutations, 25% had ERBB2 amplification, and 38% with activating PIK3CA mutations. In data from TCGA, 32% had activating ERBB2 mutations, 19% had ERBB2 amplification, and 55% had activating PIK3CA mutations. While classic ILC in TCGA had similar percentages of PIK3CA alterations compared to PLC, activating ERBB2 alterations were exceedingly rare, with no activating ERBB2 mutations and only one case with ERBB2 amplification. Interestingly, in further examining TCGA data which included FGFR1 and PTEN, 94% of PLC had alterations in ERBB2, FGFR1, or the PI3K pathway. CONCLUSIONS: Our results show a high frequency of ERBB2 and PIK3CA alterations in PLC and suggest all PLC should be tested for potential therapeutic targeting.

Use of comprehensive genomic profiling to direct point-of-care management of patients with gynecologic cancers.

OBJECTIVE: To determine the feasibility and clinical utility of using comprehensive genomic profiling (CGP) in the course of clinical care to identify clinically relevant tumor genomic alterations for patients with either rare or refractory gynecologic cancers to facilitate point-of-care management. Use of an expert, multidisciplinary, institutional molecular tumor board (MTB) assessment is discussed regarding input on putative targeted options for individualized therapy. METHODS: A prospective clinical trial is ongoing. We report on the initial 69 patients with gynecologic cancers that were either rare or refractory to standard therapy. CGP was performed by Foundation Medicine, Inc. Genomic alterations were reviewed by members of an MTB. Consensus recommendations on genomically targeted, FDA-approved, on- and off-label therapies and clinical trials were sent to the treating physician, and decisions and outcomes were assessed. RESULTS: Study outcomes were available for 64 patients. The mean number of genes altered per tumor was 4.97 (median=4; range, 1-26), and the average turnaround time from testing laboratory report to generation of formal recommendations was approximately three weeks. Evaluation of genomic and clinical data by the MTB led to generation of targeted treatment options in all 64 patients, and the percentage of patients for whom one or more of these recommendations were implemented by the treating physician was 39%. Sixty-four percent of the patients receiving targeted therapy based on a CGP result experienced radiologic response or showed evidence of clinical benefit or stable disease. CONCLUSION: These data suggest that an institutional MTB is a feasible venue for reviewing tumor genomic profiling results and generating clinical recommendations. These data also support the need for further studies and guidelines on clinical decision making with greater availability of broad genomically based diagnostics.

Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma.

Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.

Integrated metabolic and genetic analysis reveals distinct features of primary differentiated thyroid cancer and its metastatic potential in humans.

Differentiated thyroid cancer (DTC) affects thousands of lives worldwide every year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) the quality of life and might be unnecessary in indolent DTC cases. This clinical setting highlights the unmet need for a precise molecular diagnosis of DTC, which should dictate appropriate therapy. Here we propose a differential multi-omics model approach to distinguish normal gland from thyroid tumor and to indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. Based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intratumor heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. Specifically, normal and tumor thyroid tissues from these patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumor cells. Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. Well-designed, prospective translational clinical trials will ultimately show the value of this targeted molecular approach. TRANSLATIONAL RELEVANCE: In this article, we propose a new integrated metabolic, genomic, and cytopathologic methods to diagnose Differentiated Thyroid Cancer when the conventional methods failed. Moreover, we suggest metabolic and genomic markers to help predict high-risk Papillary Thyroid Cancer. Both might be important tools to avoid unnecessary surgery and/or radioiodine therapy that can worsen the quality of life of the patients more than living with an indolent Thyroid nodule.

Integrated metabolic and genetic analysis reveals distinct features of human differentiated thyroid cancer.

BACKGROUND: Differentiated thyroid cancer (DTC) affects thousands of lives worldwide each year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) quality of life and might be unnecessary in indolent DTC cases. On the other hand, the lack of biomarkers indicating a potential metastatic thyroid cancer imposes an additional challenge to managing and treating patients with this disease. AIM: The presented clinical setting highlights the unmet need for a precise molecular diagnosis of DTC and potential metastatic disease, which should dictate appropriate therapy. MATERIALS AND METHODS: In this article, we present a differential multi-omics model approach, including metabolomics, genomics, and bioinformatic models, to distinguish normal glands from thyroid tumours. Additionally, we are proposing biomarkers that could indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. RESULTS: Normal and tumour thyroid tissue from DTC patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumour cells. The consistency of the DTC metabolic profile allowed us to build a bioinformatic classification model capable of clearly distinguishing normal from tumor thyroid tissues, which might help diagnose thyroid cancer. Moreover, based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intra-tumour heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. DISCUSSION: Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. CONCLUSIONS: Well-designed, prospective translational clinical trials will ultimately show the value of this integrated multi-omics approach and early diagnosis of DTC and potential metastatic PTC.

Genomic landscape of lymphatic malformations: a case series and response to the PI3Kα inhibitor alpelisib in an N-of-1 clinical trial.

Background: Lymphatic malformations (LMs) often pose treatment challenges due to a large size or a critical location that could lead to disfigurement, and there are no standardized treatment approaches for either refractory or unresectable cases. Methods: We examined the genomic landscape of a patient cohort of LMs (n = 30 cases) that underwent comprehensive genomic profiling using a large-panel next-generation sequencing assay. Immunohistochemical analyses were completed in parallel. Results: These LMs had low mutational burden with hotspot PIK3CA mutations (n = 20) and NRAS (n = 5) mutations being most frequent, and mutually exclusive. All LM cases with Kaposi sarcoma-like (kaposiform) histology had NRAS mutations. One index patient presented with subacute abdominal pain and was diagnosed with a large retroperitoneal LM harboring a somatic PIK3CA gain-of-function mutation (H1047R). The patient achieved a rapid and durable radiologic complete response, as defined in RECIST1.1, to the PI3Kα inhibitor alpelisib within the context of a personalized N-of-1 clinical trial (NCT03941782). In translational correlative studies, canonical PI3Kα pathway activation was confirmed by immunohistochemistry and human LM-derived lymphatic endothelial cells carrying an allele with an activating mutation at the same locus were sensitive to alpelisib treatment in vitro, which was demonstrated by a concentration-dependent drop in measurable impedance, an assessment of cell status. Conclusions: Our findings establish that LM patients with conventional or kaposiform histology have distinct, yet targetable, driver mutations. Funding: R.P. and W.A. are supported by awards from the Levy-Longenbaugh Fund. S.G. is supported by awards from the Hugs for Brady Foundation. This work has been funded in part by the NCI Cancer Center Support Grants (CCSG; P30) to the University of Arizona Cancer Center (CA023074), the University of New Mexico Comprehensive Cancer Center (CA118100), and the Rutgers Cancer Institute of New Jersey (CA072720). B.K.M. was supported by National Science Foundation via Graduate Research Fellowship DGE-1143953. Clinical trial number: NCT03941782.

Clinical Features and Multiplatform Molecular Analysis Assist in Understanding Patient Response to Anti-PD-1/PD-L1 in Renal Cell Carcinoma.

Predicting response to ICI therapy among patients with renal cell carcinoma (RCC) has been uniquely challenging. We analyzed patient characteristics and clinical correlates from a retrospective single-site cohort of advanced RCC patients receiving anti-PD-1/PD-L1 monotherapy (N = 97), as well as molecular parameters in a subset of patients, including multiplexed immunofluorescence (mIF), whole exome sequencing (WES), T cell receptor (TCR) sequencing, and RNA sequencing (RNA-seq). Clinical factors such as the development of immune-related adverse events (odds ratio (OR) = 2.50, 95% confidence interval (CI) = 1.05-5.91) and immunological prognostic parameters, including a higher percentage of circulating lymphocytes (23.4% vs. 17.4%, p = 0.0015) and a lower percentage of circulating neutrophils (61.8% vs. 68.5%, p = 0.0045), correlated with response. Previously identified gene expression signatures representing pathways of angiogenesis, myeloid inflammation, T effector presence, and clear cell signatures also correlated with response. High PD-L1 expression (>10% cells) as well as low TCR diversity (≤644 clonotypes) were associated with improved progression-free survival (PFS). We corroborate previously published findings and provide preliminary evidence of T cell clonality impacting the outcome of RCC patients. To further biomarker development in RCC, future studies will benefit from integrated analysis of multiple molecular platforms and prospective validation.

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images.

Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, self-training with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.

Non-Genetic Intra-Tumor Heterogeneity Is a Major Predictor of Phenotypic Heterogeneity and Ongoing Evolutionary Dynamics in Lung Tumors.

Impacts of genetic and non-genetic intra-tumor heterogeneity (ITH) on tumor phenotypes and evolvability remain debated. We analyze ITH in lung squamous cell carcinoma at the levels of genome, transcriptome, and tumor-immune interactions and histopathological characteristics by multi-region bulk and single-cell sequencing. Genomic heterogeneity alone is a weak indicator of intra-tumor non-genetic heterogeneity at immune and transcriptomic levels that impact multiple cancer-related pathways, including those related to proliferation and inflammation, which in turn contribute to intra-tumor regional differences in histopathology and subtype classification. Tumor subclones have substantial differences in proliferation score, suggestive of non-neutral clonal dynamics. Proliferation and other cancer-related pathways also show intra-tumor regional differences, sometimes even within the same subclones. Neo-epitope burden negatively correlates with immune infiltration, indicating immune-mediated purifying selection on somatic mutations. Taken together, our observations suggest that non-genetic heterogeneity is a major determinant of heterogeneity in histopathological characteristics and impacts evolutionary dynamics in lung cancer.

Comprehensive genomic profiling aids in treatment of a metastatic endometrial cancer.

FGFR-TACC fusions, including FGFR3-TACC3, have been identified as potential oncogenic drivers and actionable alterations in a number of different cancer types. The clinical relevance of FGFR3-TACC3 fusions in endometrial cancer has not yet been described. Formalin-fixed, paraffin-embedded metastatic endometrial carcinoma from the spleen and peritoneum were sent for comprehensive genomic profiling (CGP) using the FoundationOne platform as part of a prospective tumor genomic profiling protocol. We report the identification of an FGFR3-TACC3 fusion in a case of metastatic endometrioid endometrial cancer. Other potentially actionable alterations detected in this specimen included PIK3CA T1025S and an uncharacterized rearrangement involving TSC2 The patient initially received an FGFR inhibitor as an investigational agent and experienced stable disease with complete resolution of a pelvic nodule; however, treatment had to be discontinued because of intolerable side effects. A PET/CT scan nearly 3 mo after discontinuation showed disease progression. She subsequently received the mTOR inhibitor, temsirolimus, later accompanied by letrozole, and achieved stable disease. Clinical benefit was attributed to the mTOR inhibitor as tumor stained negative for estrogen receptor. Temsirolimus was discontinued after >17 mo because of disease progression. FGFR inhibitors may have clinical benefit in the treatment of endometrial carcinoma with FGFR3-TACC3 fusions. Additionally, clinical benefit from an mTOR inhibitor may reflect a response to targeting the alteration in PIK3CA or TSC2 More research is needed to understand the activity of FGFR3-TACC3 fusions on tumors and to discover additional therapeutic options for endometrial carcinoma patients with this gene fusion.

Patient-Derived Xenograft Models of Non-Small Cell Lung Cancer and Their Potential Utility in Personalized Medicine.

Traditional preclinical studies of cancer therapeutics have relied on the use of established human cell lines that have been adapted to grow in the laboratory and, therefore, may deviate from the cancer they were meant to represent. With the emphasis of cancer drug development shifting from non-specific cytotoxic agents to rationally designed molecularly targeted therapies or immunotherapy comes the need for better models with predictive value regarding therapeutic activity and response in clinical trials. Recently, the diversity and accessibility of immunodeficient mouse strains has greatly enhanced the production and utility of patient-derived xenograft (PDX) models for many tumor types, including non-small cell lung cancer (NSCLC). Combined with next-generation sequencing, NSCLC PDX mouse models offer an exciting tool for drug development and for studying targeted therapies while utilizing patient samples with the hope of eventually aiding in clinical decision-making. Here, we describe NSCLC PDX mouse models generated by us and others, their ability to reflect the parental tumors' histomorphological characteristics, as well as the effect of clonal selection and evolution on maintaining genomic integrity in low-passage PDXs compared to the donor tissue. We also raise vital questions regarding the practical utility of PDX and humanized PDX models in predicting patient response to therapy and make recommendations for addressing those questions. Once collaborations and standardized xenotransplantation and data management methods are established, NSCLC PDX mouse models have the potential to be universal and invaluable as a preclinical tool that guides clinical trials and standard therapeutic decisions.