Cancer Patient-Reported Preferences and Knowledge for Liquid Biopsies and Blood Biomarkers at a Comprehensive Cancer Center

Background

Blood-based biomarkers (liquid biopsy) are increasingly used in precision oncology. Yet, little is known about cancer patients' perspectives in clinical practice. We explored patients' depth of preferences for liquid vs tissue biopsies and knowledge regarding the role of blood biomarkers on their cancer.

Methods

Three interviewer-administered trade-off scenarios and a 54-item self-administered questionnaire were completed by cancer outpatients across all disease sites at the Princess Margaret Cancer Centre.

Results

Of 413 patients, 54% were female; median age was 61 (range 18-101) years. In trade-off scenario preference testing, 90% (n=372) preferred liquid over tissue biopsy at baseline; when wait times for their preferred test were increased from 2 weeks, patients tolerated an additional mean of 1.8 weeks (SD 2.1) for liquid biopsy before switching to tissue biopsy (with wait time 2 weeks). Patients also tolerated a 6.2% decrease (SD 8.8) in the chance that their preferred test would conclusively determine optimal treatment before switching from the baseline of 80%. 216 patients (58%) preferred liquid biopsy even with no chance of adverse events from tissue biopsy. Patients' knowledge of blood-based biomarkers related to their cancer was low (mean 23%); however, the majority viewed development of blood biomarkers as important.

Conclusion

Patients had limited understanding of cancer-specific blood-based biomarkers, but 90% preferred liquid over tissue biopsies to assess biomarkers. There was little tolerance to wait longer for results, or for decreased test-conclusiveness. Developing accurate, low-risk tests for cancer diagnosis and management for blood biomarkers is therefore desirable to patients.

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

The increased availability of tumor genetic testing and targeted cancer therapies contributes to the advancement of precision medicine in the field of oncology. Precision oncology knowledgebases provide a way of organizing clinically relevant genetic information in a way that is easily accessible for both oncologists and patients, facilitating the genetic-based clinical decision making. Many organizations and companies have built precision oncology knowledgebases, intended for multiple users. In general, these knowledgebases offer information on cancer-related genetic variants as well as their associated diagnostic, prognostic, and therapeutic implications, but they often differ in their information curations, designs, and user experiences. It is advisable that oncologists use multiple knowledgebases during their practice to have them complement each other. In the future, convergence toward common standards and formats is needed to ensure that the comprehensive knowledge across all sources can be unified to bring the oncology community closer to the achievement of the goal of precision oncology.

Hyperactivation of TORC1 Drives Resistance to the Pan-HER Tyrosine Kinase Inhibitor Neratinib in HER2-Mutant Cancers

We developed neratinib-resistant HER2-mutant cancer cells by gradual dose escalation. RNA sequencing identified TORC1 signaling as an actionable mechanism of drug resistance. Primary and acquired neratinib resistance in HER2-mutant breast cancer patient-derived xenografts (PDXs) was also associated with TORC1 hyperactivity. Genetic suppression of RAPTOR or RHEB ablated P-S6 and restored sensitivity to the tyrosine kinase inhibitor. The combination of the TORC1 inhibitor everolimus and neratinib potently arrested the growth of neratinib-resistant xenografts and organoids established from neratinib-resistant PDXs. RNA and whole-exome sequencing revealed RAS-mediated TORC1 activation in a subset of neratinib-resistant models. DNA sequencing of HER2-mutant tumors clinically refractory to neratinib, as well as circulating tumor DNA profiling of patients who progressed on neratinib, showed enrichment of genomic alterations that converge to activate the mTOR pathway.

PARP Inhibitors in Prostate and Urothelial Cancers

Poly(ADP-ribose) polymerase (PARP) inhibitors targeting DNA repair gene mutations have shown significant clinical benefit in patients with ovarian and breast cancers. In metastatic prostate cancers, the prevalence of DNA repair gene mutations is up to 20%, and early phase studies have shown clinical activity of PARP inhibitors. Numerous clinical trials with either PARP monotherapy or in combination with other therapeutic agents are ongoing in prostate cancer. In this comprehensive review, we provide the rationale, efficacy, and safety data of PARP inhibitors in prostate as well as urothelial cancers.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Pathogenic mutations and overall survival in 3,084 patients with cancer: the Hellenic Cooperative Oncology Group Precision Medicine Initiative

Background: We evaluated the association between pathogenic mutations and overall survival (OS) in patients with cancer referred to Hellenic Cooperative Oncology Group–affiliated Departments.

Patients and methods: Patients referred from 12/1980 to 1/2017 had molecular testing (for research) of archival tumor tissue collected at the time of first diagnosis (non-metastatic, 81%; metastatic, 19%). Tumor-specific gene panels (16-101 genes) were used to identify pathogenic mutations in clinically relevant genes. NGS genotyping was performed at the Laboratory of Molecular Oncology, Aristotle University of Thessaloniki. Annotation of mutations was performed at MD Anderson Cancer Center.

Results: We analyzed 3,084 patients (median age, 57 years; men, 22%) with sequencing data. Overall, 1,775 (58% of 3,084) patients had pathogenic mutations. The median follow-up was 7.52 years (95% CI, 7.39-7.61). In patients with non-metastatic tumors, after stratification by tumor type, increasing age, higher grade, and histology other than adenocarcinoma were associated with shorter OS. OS was also shorter in patients with pathogenic TP53 (HR=1.36; p<0.001), MLL3 (HR=1.64; p=0.005), and BRCA1 (HR=1.46; p=0.047) mutations compared to wild-type genes. In multivariate analyses, independent prognostic factors predicting shorter OS were pathogenic mutations in TP53 (HR=1.37, p=0.002) and MLL3 (HR=1.50, p=0.027); increasing age (HR=1.02, p<0.001); and increasing grade (HR=1.46, p<0.001). In patients with metastatic cancer, older age and higher grade were associated with shorter OS and maintained their independent prognostic significance (increasing age, HR=1.03, p<0.001 and higher grade, HR=1.73, p<0.001).

Conclusions: Analysis of molecular data reveals prognostic biomarkers, regardless of tissue or organ of origin to improve patient management.

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients' responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the "missing link" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.

Patient and Provider Perspectives Regarding Enrollment in Head and Neck Cancer Research

OBJECTIVE

The advent of precision oncology complicates how clinicians and participants understand how clinical care and research interface. Here we examine how key stakeholders perceive the utility of, and evaluate the decision to participate in, genomic sequencing head and neck cancer research. The goal of this study was to highlight unique considerations for our community as this type of research proliferates across the country.

STUDY DESIGN

Prospective multimethod qualitative and quantitative embedded ethics protocol.

SETTING

Single-institution National Cancer Institute-designated academic cancer center.

SUBJECTS AND METHODS

Multimethod study using paired surveys and semistructured interviews among patients and providers involved in a prospective precision head and neck oncology sequencing protocol (116 survey patient-participants, response rate 82%) with 18 interviewees.

RESULTS

Participants were generally enthusiastic about enrollment in research, both to help future patients and as a way of giving back to the community. They described reliance on information from and trust in their cancer doctor regarding the decision to participate in research, but paradoxically there was discordance in how doctors and patients reported their respective influence in the decision-making process. Clinicians also stressed the importance in separating clinical and research-informed consent processes, although patients did not describe this tension.

CONCLUSION

As we enter an era of increasing personalized medicine and targeted therapies, the relationship between clinicians, scientists, and patients plays a larger role in how we individualize and contextualize cancer research. Our data are another step toward the ultimate goal of respecting and protecting patients as participants in head and neck translational oncology.

A virtual molecular tumor board to improve efficiency and scalability of delivering precision oncology to physicians and their patients

OBJECTIVES

Scalable informatics solutions that provide molecularly tailored treatment recommendations to clinicians are needed to streamline precision oncology in care settings.

MATERIALS AND METHODS

We developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations.

RESULTS

The VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly 2-fold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support.

DISCUSSION

VMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand.

CONCLUSION

Further development of such clinical augmentation tools that systematically combine patient-derived molecular data, real-world evidence from electronic health records and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care.

Engineering State-of-the-Art Plasmonic Nanomaterials for SERS-Based Clinical Liquid Biopsy Applications

Precision oncology, defined as the use of the molecular understanding of cancer to implement personalized patient treatment, is currently at the heart of revolutionizing oncology practice. Due to the need for repeated molecular tumor analyses in facilitating precision oncology, liquid biopsies, which involve the detection of noninvasive cancer biomarkers in circulation, may be a critical key. Yet, existing liquid biopsy analysis technologies are still undergoing an evolution to address the challenges of analyzing trace quantities of circulating tumor biomarkers reliably and cost effectively. Consequently, the recent emergence of cutting-edge plasmonic nanomaterials represents a paradigm shift in harnessing the unique merits of surface-enhanced Raman scattering (SERS) biosensing platforms for clinical liquid biopsy applications. Herein, an expansive review on the design/synthesis of a new generation of diverse plasmonic nanomaterials, and an updated evaluation of their demonstrated SERS-based uses in liquid biopsies, such as circulating tumor cells, tumor-derived extracellular vesicles, as well as circulating cancer proteins, and tumor nucleic acids is presented. Existing challenges impeding the clinical translation of plasmonic nanomaterials for SERS-based liquid biopsy applications are also identified, and outlooks and insights into advancing this rapidly growing field for practical patient use are provided.

Pan-cancer RNA-seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue-independent anticancer therapeutic targets. We hypothesized that RNA-Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA-TARGET-GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co-correlated 'modules' from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK-cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour-promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA-Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.

Precision medicine, agriculture, and genome editing: science and ethics

The era of precision medicine has generated advances in various fields of science and medicine with the potential for a paradigm shift in healthcare delivery that will ultimately lead to an individualized approach to medicine. Such timely topics were explored in 2018 at a workshop held at the Third International Conference on One Medicine One Science (iCOMOS), in Minneapolis, Minnesota. A broad range of scientists and regulatory experts provided detailed insights into the challenges and opportunities associated with precision medicine and gene editing. There was a general consensus that advances in studying the genomic traits driving differential pharmacogenomics will undoubtedly enhance individualized treatments for a wide variety of diseases. Ethical considerations, societal implications, approaches for prioritizing safe and secure use of treatment modalities, and the advent of high-throughput computing and analysis of large, complex datasets were discussed. Large biobanks, such as the All of Us Research Program and the Veterans Affairs Million Veterans Program, can aid studies of various conditions in massive cohorts of patients. As the applications of precision medicine continue to mature, the full potential and promise of these individualized approaches will continue to yield important advances in transplant medicine, oncology, public health, agriculture, pharmacology, and bioinformatics.

Enabling Precision Medicine for Rare Head and Neck Tumors: The Example of BRAF/MEK Targeting in Patients With Metastatic Ameloblastoma

Background: Ameloblastoma is a rare head and neck tumor characterized by a high incidence of BRAF mutation providing a rationale for the use of BRAF inhibitors in patients with advanced disease. Methods: We report the case of a 26-year old female presenting with metastatic ameloblastoma. A molecular screening of the tumor revealed a BRAF V600E mutation. Results: The patient started treatment with dabrafenib and trametinib and experienced complete response which is still ongoing 30 weeks after treatment onset. Conclusions: The complete response observed here illustrate the role of molecular profiling in complicate clinical situation of rare head and neck cancer and the potential benefit of BRAF-targeted therapy in ameloblastoma carrying BRAF V600E mutation.

Durable response to palbociclib and letrozole in ovarian cancer with CDKN2A loss

Alterations of the Retinoblastoma (Rb) pathway are frequent in ovarian cancer, typically resulting from CDKN2A down-regulation, CCNE1 amplification, CCND1/2 amplification, and RB1 loss. However, bi-allelic CDKN2A mutation or homozygous deletion is a very rare event, concerning less than 5% of patients.Initial trials with palbociclib in serous ovarian cancer have shown very modest benefit in unselected patient populations, thus underlining the need for a biomarker predicting response. We report the case of a heavily pre-treated patient with a serous ovarian tumor harboring a homozygous deletion of the CDKN2A gene that derived significant, prolonged clinical benefit from palbociclib, a CDK4/6 oral inhibitor, with letrozole. Treatment with palbociclib and letrozole started on February 2018, with an ongoing response after 12 months.In conclusion, homozygous CDKN2A deletion is rare and could be used to predict response to CDK4/6 inhibitors in association with other genomic features. We encourage further trials in this direction.

Why Are Some Driver Mutations Rare?

Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.

Clinical activity of the EGFR tyrosine kinase inhibitor osimertinib in EGFR-mutant glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and carries a dismal prognosis. The EGFR gene is among the most commonly deranged genes in GBM and thus an important therapeutic target. We report the case of a young female with heavily pretreated EGFR-mutated GBM, for whom we initiated osimertinib, an oral, third-generation tyrosine kinase inhibitor that irreversibly inhibits EGFR and has significant brain penetration. We then review some of the main challenges in targeting EGFR, including lack of central nervous system penetration with most tyrosine kinase inhibitors, molecular heterogeneity of GBM and the need for enhanced specificity for the EGFR mutations relevant in GBM.

Light and shadow on innovative clinical trial designs: reflections from the EORTC-PAMM course on 'preclinical and early-phase clinical pharmacology'

Introduction: In the era of 'precision' oncology, novel clinical trial designs have emerged, in order to better address the final goal of translating the above-mentioned preclinical discoveries into the clinic. Nonetheless, in aiming to achieve the greatest clinical benefit to patients, some limitations of these novel approaches from the statistical, methodological and practical point of view need to be overcome.Areas covered: In the present review, a short overview of basket trials, umbrella trials and platform trials are discussed, in particular advantages and disadvantages of such experimental approaches.Expert opinion: Master protocols represent the future of clinical oncology research. The possibility of investigating multiple biomarkers and therapeutic regimens under one study is a strong advantage over traditional trials, and it can lead to quick implementation of new, promising treatments or biomarkers into the clinic.

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?

Tumor cells detach from the primary tumor or metastatic sites and enter the peripheral blood, often causing metastasis. These cells, named Circulating Tumor Cells (CTCs), display the same spatial and temporal heterogeneity as the primary tumor. Since CTCs are involved in tumor progression, they represent a privileged window to disclose mechanisms of metastases, while -omic analyses at the single-cell level allow dissection of the complex relationships between the tumor subpopulations and the surrounding normal tissue. However, in addition to reporting the proof of concept that we can query CTCs to reveal tumor evolution throughout the continuum of treatment for early detection of resistance to therapy, the scientific literature has also been highlighting the disadvantages of CTCs, which hampers a routine use of this approach in clinical practice. To date, an increasing number of CTC technologies, as well as -omics methods, have been employed, mostly lacking strong comparative analyses. The rarity of CTCs also represents a major challenge, because there is no consensus regarding the minimal criteria necessary and sufficient to define an event as CTC; moreover, we cannot often compare data from of one study with that of another. Finally, the availability of an individual tumor profile undermines the traditional histology-based treatment. Applying molecular data for patient benefit implies a collective effort by biologists, bioengineers, and clinicians, to create tools to interpret molecular data and manage precision medicine in every single patient. Herein, we focus on the most recent findings in CTC −omics to learn how far we have come.

Gene signature predictive of hepatocellular carcinoma patient response to transarterial chemoembolization

Transarterial chemoembolization (TACE) is a commonly used treatment modality in hepatocellular carcinoma (HCC). The ability to identify patients who will respond to TACE represents an important clinical need, and tumor gene expression patterns may be associated with TACE response. We investigated whether tumor transcriptome is associated with TACE response in patients with HCC. We analyzed transcriptome data of treatment-naïve tumor tissues from a Chinese cohort of 191 HCC patients, including 105 patients who underwent TACE following resection with curative intent. We then developed a gene signature, TACE Navigator, which was associated with improved survival in patients that received either adjuvant or post-relapse TACE. To validate our findings, we applied our signature in a blinded manner to three independent cohorts comprising an additional 130 patients with diverse ethnic backgrounds enrolled in three different hospitals who received either adjuvant TACE or palliative TACE.

TACE Navigator stratified patients into Responders and Non-Responders which was associated with improved survival following TACE in our test cohort (Responders: 67 months vs Non-Responders: 39.5 months, p<0.0001). In addition, multivariable Cox model demonstrates that TACE Navigator was independently associated with survival (HR: 9.31, 95% CI: 3.46-25.0, p<0.001). In our validation cohorts, the association between TACE Navigator and survival remained robust in both Asian patients who received adjuvant TACE (Hong Kong: 60 months vs 25.6 months p=0.007; Shandong: 61.3 months vs 32.1 months, p=0.027) and European patients who received TACE as primary therapy (Mainz: 60 months vs 41.5 months, p=0.041). These results indicate that a TACE-specific molecular classifier is robust in predicting TACE response. This gene signature can be used to identify patients who will have the greatest survival benefit after TACE treatment and enable personalized treatment modalities for patients with HCC.

The Need for Multi-Omics Biomarker Signatures in Precision Medicine

Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses—which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas—have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.

Encyclopedic tumor analysis for guiding treatment of advanced, broadly refractory cancers: results from the RESILIENT trial

RESILIENT (CTRI/2018/02/011808) was a single arm, open label, phase II/III study to test if label agnostic therapy regimens guided by Encyclopedic Tumor Analysis (ETA) can offer meaningful clinical benefit for patients with relapsed refractory metastatic (r/r-m) malignancies. Patients with advanced refractory solid organ malignancies where disease had progressed following ≥2 lines of systemic treatments were enrolled in the trial. Patients received personalized treatment recommendations based on integrational comprehensive analysis of freshly biopsied tumor tissue and blood. The primary end points were Objective Response Rate (ORR), Progression Free Survival (PFS) and Quality of Life (QoL). Objective Response (Complete Response + Partial Response) was observed in 54 of 126 patients evaluable per protocol (ORR = 42.9%; 95% CI: 34.3%–51.4%, p < 0.0001). At study completion, Disease Control (Complete Response + Partial Response + Stable Disease) was observed in 114 out of 126 patients evaluable per protocol (CBR = 90.5%; 95% CI: 83.9% - 95.0%, p < 0.00001) and Disease Progression in 12 patients. Median duration of follow-up was 138 days (range 31 to 379). Median PFS at study termination was 134 days (range 31 to 379). PFS rate at 90 days and 180 days were 93.9% and 82.5% respectively. The study demonstrated that tumors have latent vulnerabilities that can be identified via integrational multi-analyte investigations such as ETA. This approach identified viable treatment options that could yield meaningful clinical benefit in this cohort of patients with advanced refractory cancers.

The promises and challenges of patient-derived tumor organoids in drug development and precision oncology

In the era of precision medicine, cancer researchers and oncologists are eagerly searching for more realistic, cost effective, and timely tumor models to aid drug development and precision oncology. Tumor models that can faithfully recapitulate the histological and molecular characteristics of various human tumors will be extremely valuable in increasing the successful rate of oncology drug development and discovering the most efficacious treatment regimen for cancer patients. Two-dimensional (2D) cultured cancer cell lines, genetically engineered mouse tumor (GEMT) models, and patient-derived tumor xenograft (PDTX) models have been widely used to investigate the biology of various types of cancers and test the efficacy of oncology drug candidates. However, due to either the failure to faithfully recapitulate the complexity of patient tumors in the case of 2D cultured cancer cells, or high cost and untimely for drug screening and testing in the case of GEMT and PDTX, new tumor models are urgently needed. The recently developed patient-derived tumor organoids (PDTO) offer great potentials in uncovering novel biology of cancer development, accelerating the discovery of oncology drugs, and individualizing the treatment of cancers. In this review, we will summarize the recent progress in utilizing PDTO for oncology drug discovery. In addition, we will discuss the potentials and limitations of the current PDTO tumor models.

A Multi-parametric MRI-Based Radiomics Signature and a Practical ML Model for Stratifying Glioblastoma Patients Based on Survival Toward Precision Oncology

Purpose: Predicting patients' survival outcomes is recognized of key importance to clinicians in oncology toward determining an ideal course of treatment and patient management. This study applies radiomics analysis on pre-operative multi-parametric MRI of patients with glioblastoma from multiple institutions to identify a signature and a practical machine learning model for stratifying patients into groups based on overall survival.

Methods: This study included 163 patients' data with glioblastoma, collected by BRATS 2018 Challenge from multiple institutions. In this proposed method, a set of 147 radiomics image features were extracted locally from three tumor sub-regions on standardized pre-operative multi-parametric MR images. LASSO regression was applied for identifying an informative subset of chosen features whereas a Cox model used to obtain the coefficients of those selected features. Then, a radiomics signature model of 9 features was constructed on the discovery set and it performance was evaluated for patients stratification into short- (<10 months), medium- (10–15 months), and long-survivors (>15 months) groups. Eight ML classification models, trained and then cross-validated, were tested to assess a range of survival prediction performance as a function of the choice of features.

Results: The proposed mpMRI radiomics signature model had a statistically significant association with survival (P < 0.001) in the training set, but was not confirmed (P = 0.110) in the validation cohort. Its performance in the validation set had a sensitivity of 0.476 (short-), 0.231 (medium-), and 0.600 (long-survivors), and specificity of 0.667 (short-), 0.732 (medium-), and 0.794 (long-survivors). Among the tested ML classifiers, the ensemble learning model's results showed superior performance in predicting the survival classes, with an overall accuracy of 57.8% and AUC of 0.81 for short-, 0.47 for medium-, and 0.72 for long-survivors using the LASSO selected features combined with clinical factors.

Conclusion: A derived GLCM feature, representing intra-tumoral inhomogeneity, was found to have a high association with survival. Clinical factors, when added to the radiomics image features, boosted the performance of the ML classification model in predicting individual glioblastoma patient's survival prognosis, which can improve prognostic quality a further step toward precision oncology.

FDG-PET/CT for Response Monitoring in Metastatic Breast Cancer: Today, Tomorrow, and Beyond

While current international guidelines include imaging of the target lesion for response monitoring in metastatic breast cancer, they do not provide specific recommendations for choice of imaging modality or response criteria. This is important as clinical decisions may vary depending on which imaging modality is used for monitoring metastatic breast cancer. FDG-PET/CT has shown high accuracy in diagnosing metastatic breast cancer, and the Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) have shown higher predictive values than the CT-based Response Evaluation Criteria in Solid Tumors (RECIST) for prediction of progression-free survival. No studies have yet addressed the clinical impact of using different imaging modalities or response evaluation criteria for longitudinal response monitoring in metastatic breast cancer. We present a case study of a patient with metastatic breast cancer who was monitored first with conventional CT and then with FDG-PET/CT. We retrospectively applied PERCIST to evaluate the longitudinal response to treatment. We used the one-lesion PERCIST model measuring SULpeak in the hottest metastatic lesion on consecutive scans. This model provides a continuous variable that allows graphical illustration of disease fluctuation along with response categories. The one-lesion PERCIST approach seems able to reflect molecular changes and has the potential to support clinical decision-making. Prospective clinical studies addressing the clinical impact of PERCIST in metastatic breast cancer are needed to establish evidence-based recommendations for response monitoring in this disease.

Next-generation sequencing for the general cancer patient

Next-generation sequencing is a novel method of DNA sequencing that has become a cornerstone of precision oncology. This sequencing method detects differences in specific DNA sequences between a sample and a reference genome or matched normal DNA. In addition to single-nucleotide variants, other insertions, deletions, copy number changes, and fusions may be drivers of cancer growth, and thus represent therapeutic opportunities. As a result, genomic characterization has been increasingly used to guide treatment decisions, especially in patients with advanced disease. This review discusses the basic technologies involved in next-generation sequencing, the applications of this method, and limitations in the clinical realm.

Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop transcriptomic signatures for specific drugs. We analyzed lung adenocarcinoma patient data from The Cancer Genome Atlas and identified a subset of patients in which xanthine dehydrogenase (XDH) expression correlated with decreased survival. We tested allopurinol, an FDA‐approved drug that inhibits XDH, on human non‐small‐cell lung cancer (NSCLC) cell lines obtained from the Broad Institute Cancer Cell Line Encyclopedia and identified sensitive and resistant cell lines. We utilized the transcriptomic profiles of these cell lines to identify six‐gene signatures for allopurinol‐sensitive and allopurinol‐resistant cell lines. Transcriptomic networks identified JAK2 as an additional target in allopurinol‐resistant lines. Treatment of resistant cell lines with allopurinol and CEP‐33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP‐33779 was verified in vivo using tumor formation in NCR‐nude mice. We utilized the six‐gene signatures to predict five additional allopurinol‐sensitive NSCLC cell lines and four allopurinol‐resistant cell lines susceptible to combination therapy. We searched the transcriptomic data from a library of patient‐derived NSCLC tumors from the Jackson Laboratory to identify tumors that would be predicted to be sensitive to allopurinol or allopurinol + CEP‐33779 treatment. Patient‐derived tumors showed the predicted drug sensitivity in vivo. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

PDGRFB mutation-associated myofibromatosis: Response to targeted therapy with imatinib

Heterozygous activating mutations in platelet-derived growth factor receptor B (PDGFRB) have been recently identified as a cause of autosomal-dominant infantile myofibromatosis. We describe a 36-year-old man with PDGFRB c.1681C>T (p.R561C) mutation. Upon progressive disease, the patient received treatment with imatinib and showed a remarkable response with remission of multiple lesions after 12 months. This is the first report of an adult patient with PDGFRB c.1681C>T mutation treated with imatinib.

Adaptive, Iterative, Long-Term Personalized Therapy Management in a Case of Stage IV Refractory NSCLC

In this paper we report long-term therapy management based on iterative de novo molecular and cellular analysis in a case of metastatic non-small cell lung cancer (NSCLC), with prior history of treated colorectal cancer. In the described case temporal tumor evolution, emergent therapy resistance and disease recurrences were addressed via the administration of personalized label- and organ-agnostic treatments based on de novo tumor profiling. This adaptive and iterative treatment strategy countered disease progression at each instance and led to the durable regression of primary as well as metastatic lesions. Concurrently, serial evaluation of mutations in cell-free circulating tumor DNA (ctDNA) via liquid biopsy (LBx) was performed to monitor disease status, ascertain treatment response, identify emergent drug resistance and detect recurrence at sub-radiological levels. The treatment management strategy described herein effectively addressed multiple, sequential clinical conundrums for which viable options were unavailable under the current Standard of Care (SoC).

An image-based deep learning framework for individualizing radiotherapy dose

Background

Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose.

Methods

We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We validated our findings in an independent study population (n = 95). Deep Profiler was combined with clinical variables to derive iGray, an individualized dose that estimates treatment failure probability to be <5%.

Findings

Radiation treatments in patients with high Deep Profiler scores fail at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 20.3% (95% CI: 16.0-24.9) and 5.7% (95% CI: 3.5-8.8), respectively. Deep Profiler independently predicted local failure (hazard ratio 1.65, 95% 1.02-2.66, p = 0.04). Models that included Deep Profiler and clinical variables predicted treatment failures with a concordance index of 0.72 (95% CI: 0.67-0.77), a significant improvement compared to classical radiomics or clinical variables alone (p = <0.001 and <0.001, respectively). Deep Profiler performed well in an external study population (n = 95), accurately predicting treatment failures across diverse clinical settings and CT scanner types (concordance index = 0.77 [95% CI: 0.69-0.92]). iGray had a wide dose range (21.1-277 Gy, BED), suggested dose reductions in 23.3% of patients and can be safely delivered in the majority of cases.

Interpretation

Our results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy. The image-based deep learning framework proposed herein is the first opportunity to use medical images to individualize radiotherapy dose.

Cancer Treatment in the Genomic Era

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

Preclinical Modelling of PDA: Is Organoid the New Black?

Pancreatic ductal adenocarcinoma (PDA) is a malignancy of the exocrine pancreas with the worst prognosis among all solid tumours, and soon to become the second leading cause of cancer-related deaths. A more comprehensive understanding of the molecular mechanisms underlying this disease is crucial to the development of diagnostic tools as well as to the identification of more effective therapies. High-frequency mutations in PDA occur in “undruggable” genes, and molecular subtyping based on bulk transcriptome analysis does not yet nominate valid therapeutic intervention strategies. Genome-wide sequencing studies have also demonstrated a considerable intra- and inter-patient’s genetic heterogeneity, which further complicate this dire scenario. More than in other malignancies, functionalization of the PDA genome and preclinical modelling at the individual patient level appear necessary to substantially improve survival rates for pancreatic cancer patients. Traditional human PDA models, including monolayer cell cultures and patient-derived xenografts, have certainly led to valuable biological insights in the past years. However, those model systems suffer from several limitations that have contributed to the lack of concordance between preclinical and clinical studies for PDA. Pancreatic ductal organoids have recently emerged as a reliable culture system to establish models from both normal and neoplastic pancreatic tissues. Pancreatic organoid cultures can be efficiently generated from small tissue biopsies, which opens up the possibility of longitudinal studies in individual patients. A proof-of-concept study has demonstrated that patient-derived PDA organoids are able to predict responses to conventional chemotherapy. The use of this three-dimensional culture system has already improved our understanding of PDA biology and promises to implement precision oncology by enabling the alignment of preclinical and clinical platforms to guide therapeutic intervention in PDA.

Importance of genetic screens in precision oncology

Precision oncology aims to distinguish which patients are eligible for a specific treatment in order to achieve the best possible outcome. In the last few years, genetic screens have shown their potential to find the new targets and drug combinations as well as predictive biomarkers for response and/or resistance to cancer treatment. In this review, we outline how precision oncology is changing over time and describe the different applications of genetic screens. Finally, we present some practical examples that describe the utility and the limitations of genetic screens in precision oncology.

Immune Checkpoint Blockade for Advanced NSCLC: A New Landscape for Elderly Patients

The therapeutic scenario for elderly patients with advanced NSCLC has been limited to radiotherapy and chemotherapy. Recently, a novel therapeutic approach based on targeting the immune-checkpoints has showed noteworthy results in advanced NSCLC. PD1/PD-L1 pathway is co-opted by tumor cells through the expression of PD-L1 on the tumor cell surface and on cells within the microenvironment, leading to suppression of anti-tumor cytolytic T-cell activity by the tumor. The success of immune-checkpoints inhibitors in clinical trials led to rapid approval by the FDA and EMA. Currently, data regarding efficacy and safety of ICIs in older subjects is limited by the poor number of elderly recruited in clinical trials. Careful assessment and management of comorbidities is essential to achieve better outcomes and limit the immune related adverse events in elderly NSCLC patients.

Precision oncology in Latin America: current situation, challenges and perspectives

BACKGROUND

Anti-cancer cytotoxic treatments like platinum-derived compounds often show low therapeutic efficacy, high-risk side effects and resistance. Hence, targeted treatments designed to attack only tumour cells avoiding these harmful side effects are highly needed in clinical practice. Due to this, precision oncology has arisen as an approach to specifically target alterations present only in cancer cells, minimising side effects for patients. It involves the use of molecular biomarkers present in each kind of tumour for diagnosis, prognosis and treatment. Since these biomarkers are specific for each cancer type, physicians use them to stratify, diagnose or take the best therapeutic options for each patient depending on the features of the specific tumour.

AIM

This review aims to describe the current situation, limitations, advantages and perspectives about precision oncology in Latin America.

MAIN BODY

For many years, many biomarkers have been used in a clinical setting in developed countries. However, in Latin American countries, their broad application has not been affordable partially due to financial and technical limitations associated with precarious health systems and poor access of low-income populations to quality health care. Furthermore, the genetic mixture in Latin American populations could generate differences in treatment responses from one population to another (pharmacoethnicity) and this should be evaluated before establishing precision therapy in particular populations. Some research groups in the region have done a lot of work in this field and these data should be taken as a starting point to establish networks oriented to finding clinically useful cancer biomarkers in Latin American populations.

CONCLUSION

Latin America must create policies allowing excluded populations to gain access to health systems and next generation anti-cancer drugs, i.e. high-cost targeted therapies to improve survival. Also, cancer clinical research must be oriented to establish cancer biomarkers adapted to specific populations with different ethnicity, allowing the improvement of patient outcomes.

A network approach to developing immuno-oncology combinations in Canada

Immune checkpoint inhibitors have revolutionized care for many cancer indications, with considerable effort now being focused on increasing the rate, depth, and duration of patient response. One strategy is to combine immune strategies (for example, ctla-4 and PD-1/L1-directed agents) to harness additive or synergistic efficacy while minimizing toxicity. Despite encouraging results with such combinations in multiple tumour types, numerous clinical challenges remain, including a lack of biomarkers that reliably predict outcome, the emergence of therapeutic resistance, and optimal management of immune-related toxicities. Furthermore, the selection of ideal combinations from the myriad of immune, systemic, and locoregional therapies has yet to be determined. A longitudinal network-based approach could offer advantages in addressing those critical questions, including long-term follow-up of patients beyond individual trials. The molecular cancer registry Personalize My Treatment, managed by the Networks of Centres of Excellence nonprofit organization Exactis Innovation, is uniquely positioned to accelerate Canadian immuno-oncology (io) research efforts throughout its national network of cancer sites. To gain deeper insight into how a pan-Canadian network could advance research in io combinations, Exactis invited preeminent clinical and scientific advisors from across Canada to a roundtable event in November 2017. The present white paper captures the expert advice provided: leverage longitudinal patient data collection; facilitate network collaboration and assay harmonization; synergize with existing initiatives, networks, and biobanks; and develop an io combination trial based on Canadian discoveries.

Feasibility and clinical utility of endoscopic ultrasound guided biopsy of pancreatic cancer for next-generation molecular profiling

Next-generation sequencing is enabling molecularly guided therapy for many cancer types, yet failure rates remain relatively high in pancreatic cancer (PC). The aim of this study is to investigate the feasibility of genomic profiling using endoscopic ultrasound (EUS) biopsy samples to facilitate personalised therapy for PC. Ninty-five patients underwent additional research biopsies at the time of diagnostic EUS. Diagnostic formalin-fixed (FFPE) and fresh frozen EUS samples underwent DNA extraction, quantification and targeted gene sequencing. Whole genome (WGS) and RNA sequencing was performed as proof of concept. Only 2 patients (2%) with a diagnosis of PC had insufficient material for targeted sequencing in both FFPE and frozen specimens. Targeted panel sequencing (n=54) revealed mutations in PC genes (KRAS, GNAS, TP53, CDKN2A, SMAD4) in patients with histological evidence of PC, including potentially actionable mutations (BRCA1, BRCA2, ATM, BRAF). WGS (n=5) of EUS samples revealed mutational signatures that are potential biomarkers of therapeutic responsiveness. RNA sequencing (n=35) segregated patients into clinically relevant molecular subtypes based on transcriptome. Integrated multi-omic analysis of PC using standard EUS guided biopsies offers clinical utility to guide personalized therapy and study the molecular pathology in all patients with PC.

Liquid Biopsies in Cancer Diagnosis, Monitoring, and Prognosis

Liquid biopsies, comprising the noninvasive analysis of circulating tumor-derived material (the 'tumor circulome'), represent an innovative tool in precision oncology to overcome current limitations associated with tissue biopsies. Within the tumor circulome, circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) are the only components the clinical application of which is approved by the US Food and Drug Administration (FDA). Extracellular vesicles (EVs), circulating tumor RNA (ctRNA), and tumor-educated platelets (TEPs) are relatively new tumor circulome constituents with promising potential at each stage of cancer management. Here, we discuss the clinical applications of each element of the tumor circulome and the prevailing factors that currently limit their implementation in clinical practice. We also detail the most recent technological developments in the field, which demonstrate potential in improving the clinical value of liquid biopsies.

Budget impact analysis of comprehensive genomic profiling in patients with advanced non-small cell lung cancer

AIMS

Broad molecular profiling of patients with advanced non-small cell lung cancer (NSCLC) is strongly advised to optimize genomic matching with available targeted treatment options or investigational agents. Unlike conventional molecular diagnostic testing, or smaller hotspot panels, comprehensive genomic profiling (CGP) identifies genomic alterations across hundreds of clinically relevant cancer genes from a single tissue specimen. The present study sought to estimate the budget impact of increased use of CGP using a 324-gene panel (FoundationOne) vs non-CGP (represented by a mix of conventional molecular diagnostic testing and smaller NGS hotspot panels) and the number needed to test with CGP to gain 1 life year.

MATERIALS AND METHODS

A decision analytic model was developed to assess the budget impact of increased CGP in advanced NSCLC from a US private payer perspective. Model inputs were based on published literature (epidemiology and treatment outcomes), real-world data (testing and rates, medical service costs), list prices for CGP and anti-cancer drugs, and assumptions for clinical trial participation.

RESULTS

Among 2 million covered lives, 532 had advanced NSCLC; 266 underwent molecular diagnostic testing. An increase in CGP among those tested, from 2% to 10%, was associated with $0.02 per member per month budget impact, of which $0.013 was attributable to costs of prolonged drug treatment and survival and $0.005 to testing cost. Approximately 12 patients would need to be tested with CGP to add 1 life year.

LIMITATIONS

The model incorporated certain assumptions to account for inputs with a limited evidence profile and simplify the possible post-CGP treatments.

CONCLUSIONS

An increase in CGP utilization from 2% to 10% among patients with advanced NSCLC undergoing molecular diagnostic testing was associated with a modest budget impact, most of which was attributable to increased use of more effective treatments and prolonged survival.

Clinical Utilization Pattern of Liquid Biopsies (LB) to Detect Actionable Driver Mutations, Guide Treatment Decisions and Monitor Disease Burden During Treatment of 33 Metastatic Colorectal Cancer (mCRC) Patients (pts) at a Fox Chase Cancer Center GI Oncology Subspecialty Clinic

Background: Liquid biopsy (LB) captures dynamic genomic alterations (alts) across metastatic colorectal cancer (mCRC) therapy and may complement tissue biopsy (TB). We sought to describe the utility of LB and better understand mCRC biology during therapy.

Methods: Thirty-three patients (pts) with mCRC underwent LB. We used permutation-based t-tests to assess associations between alts, and clinical variables and used Kendall's tau to measure correlations.

Results: Of 33 pts, 15 were women; 22 had colon, and the rest rectal cancer. Pts received a median of two lines of therapy before LB. Nineteen pts had limited testing on TB (RAS/RAF/TP53/APC), 11 extended NGS, and 3 no TB. Maxpct and alts correlated with CEA (p < 0.001, respectively). In 3/5 pts with serial LB, CEA correlated with maxpct trend, and CT tumor burden. In 6 pts, mutant RAS was seen in LB and not TB; 5/6 had received anti-EGFR therapy prior to LB, suggesting RAS alts developed post-therapy. In two pts RAS-mutated by TB, no RAS alts were detected on LB; these pts had low disease burden on CT at time of LB that also did not reveal APC or TP53 alts. In six patients who were KRAS wt based on TB, post anti-EGFR LB revealed subclonal KRAS mutations, likely a treatment effect. The median number of alts was higher post anti-EGFR LB (n = 12) vs. anti-EGFR nave LB (n = 22) (9.5 vs. 5.5, p = 0.059) but not statistically significant. More alts were also noted in post anti-EGFR therapy LB vs. KRAS wt anti-EGFR-nave LB (n = 6) (9.5 vs. 5) among patients with KRAS wild-type tumors, although the difference was not significant (p = 0.182).

Conclusions: LB across mCRC therapy detects driver mutations, monitors disease burden, and identifies sub-clonal alts that reflect drug resistance, tumor evolution, and heterogeneity. Interpretation of LB results is impacted by clinical context.

Next-generation sequencing in precision oncology: Patient understanding and expectations

BACKGROUND

Implementation of precision oncology interventions poses several challenges to informed consent and patient education. This study assessed cancer patients' understanding, expectations, and outcomes regarding participation in research examining the impact of matched tumor and germline sequencing on their clinical care.

METHODS

A total of 297 patients (mean age: 59 years; 50% female; 96% white) with refractory, metastatic cancer were surveyed, including 217 who completed surveys both before and after undergoing integrated whole exome and transcriptome sequencing as part of a larger clinical research study.

RESULTS

At baseline, the vast majority of patients expected to receive several potential direct benefits from study participation, including written reports of sequencing findings (88%), greater understanding of the causes of their cancer (74%), and participation in clinical trials for which sequencing results would make them eligible (84%). In most cases, these benefits were not realized by study completion. Despite explanations from study personnel to the contrary, most participants (67%-76%) presumed that incidental germline sequencing findings relevant to noncancerous health conditions (eg, diabetes) would automatically be disclosed to them. Patients reported low levels of concern about study risks at baseline and low levels of regret about study participation at follow-up.

CONCLUSIONS

Findings suggest that cancer patients participating in precision oncology intervention research have largely unfulfilled expectations of direct benefits related to their study participation. Increased focus on patient education to supplement the informed consent process may help manage patients' expectations regarding the extent and likelihood of benefits received as a result of undergoing genomic sequencing.

Phosphatidylinositol 3-kinase pathway genomic alterations in 60,991 diverse solid tumors informs targeted therapy opportunities

Background

The phosphatidylinositol 3‐kinase (PI3K) pathway is frequently altered in cancer. This report describes the landscape of PI3K alterations in solid tumors as well as co‐alterations serving as potential resistance/attenuation mechanisms.

Methods

Consecutive samples were analyzed in a commercial Clinical Laboratory Improvement Amendment‐certified laboratory using comprehensive genomic profiling performed by next‐generation sequencing (315 genes). The co‐alterations evaluated included the Erb‐B2 receptor tyrosine kinase 2 (ERBB2), ERBB3, ERBB4, RAS, MET proto‐oncogene tyrosine kinase (MET), and mitogen‐activated protein kinase kinase (MAP2K) genes as well as tumor protein 53 (TP53), estrogen receptor 1 (ESR1), and androgen receptor (AR).

Results

Alterations in any of 18 PI3K‐pathway associated genes were identified in 44% of 60,991 tumors. Although single base and insertions/deletions (indels) were the most frequent alterations, copy number changes and rearrangements were identified in 11% and 0.9% of patients, respectively. Overall, the most frequently altered genes were PIK3 catalytic subunit α (PIK3CA) (13%), phosphatase and tensin homolog (PTEN) (9%), and serine/threonine kinase 11 (STK11) (5%). Tumor types that frequently harbored at least 1 PI3K alteration were uterine (77%), cervical (62%), anal (59%), and breast (58%) cancers. Alterations also were discerned frequently in tumors with carcinosarcoma (89%) and squamous cell carcinoma (62%) histologies. Tumors with a greater likelihood of co‐occurring PI3K pathway and MAPK pathway alterations included colorectal cancers (odds ratio [OR], 1.64; P < .001), mesotheliomas (OR, 2.67; P = .024), anal cancers (OR, 1.98; P = .03), and nonsquamous head and neck cancers (OR, 2.03; P = .019). The co‐occurrence of ESR1 and/or AR alterations with PI3K alterations was statistically significant in bladder, colorectal, uterine, prostate, and unknown primary cancers.

Conclusions

Comprehensive genomic profiling reveals altered PI3K‐related genes in 44% of solid malignancies, including rare disease and histology types. The frequency of alterations and the co‐occurrence of resistance pathways vary by tumor type, directly affecting opportunities for targeted therapy.

Comprehensive genomic profiling of solid tumors reveals frequent genetic alterations in several genes of the phosphatidylinositol 3‐kinase (PI3K) pathway. Data from this analysis suggest that in‐depth characterization of the PI3K pathway along with concomitant resistance alterations in other pathways can provide a genomic background for the development of future treatments.

The identification of patient-specific mutations reveals dual pathway activation in most patients with melanoma and activated receptor tyrosine kinases in BRAF/NRAS wild-type melanomas

BACKGROUND

Increasing knowledge of cancer genomes has triggered the development of specific targeted inhibitors, thus providing a valuable therapeutic pool.

METHODS

In this report, the authors analyze the presence of targetable alterations in 136 tumor samples from 92 patients with melanoma using a comprehensive approach based on targeted DNA sequencing and supported by RNA and protein analysis. Three topics of high clinical relevance are addressed: the identification of rare, activating alterations; the detection of patient-specific, co-occurring single nucleotide variants (SNVs) and copy number variations (CNVs) in parallel pathways; and the presence of cancer-relevant germline mutations.

RESULTS

The analysis of patient-matched blood and tumor samples was done with a custom-designed gene panel that was enriched for genes from clinically targetable pathways. To detect alterations with high therapeutic relevance for patients with unknown driver mutations, genes that are untypical for melanoma also were included. Among all patients, CNVs were identified in one-third of samples and contained amplifications of druggable kinases, such as CDK4, ERBB2, and KIT. Considering SNVs and CNVs, 60% of patients with metastases exhibited co-occurring activations of at least 2 pathways, thus providing a rationale for individualized combination therapies. Unexpectedly, 9% of patients carry potentially protumorigenic germline mutations frequently affecting receptor tyrosine kinases. Remarkably two-thirds of BRAF/NRAS wild-type melanomas harbor activating mutations or CNVs in receptor tyrosine kinases.

CONCLUSIONS

The results indicate that the integrated analysis of SNVs, CNVs, and germline mutations reveals new druggable targets for combination tumor therapy.

Exploring the feasibility and utility of exome-scale tumour sequencing in a clinical setting

BACKGROUND

Technology has progressed from single gene panel to large-scale genomic sequencing. This is raising expectations from clinicians and patients alike. The utility and performance of this technology in a clinical setting needs to be evaluated.

AIM

This pilot study investigated the feasibility of using exome-scale sequencing (ESS) to identify molecular drivers within cancers in real-time for Precision Oncology in the clinic.

METHODS

Between March 2014 and March 2015, the Victorian Comprehensive Cancer Centre Alliance explored the feasibility and utility of ESS in a pilot study. DNA extracted from the tumour specimens underwent both ESS and targeted 'hotspot' sequencing (TS). Blood was taken for germline analysis. A multi-disciplinary molecular tumour board determined the clinical relevance of identified mutations; in particular, whether they were 'actionable' and/or 'druggable'.

RESULTS

Of 23 patients screened, 15 (65%) met the tissue requirements for genomic analysis. TS and ESS were successful in all cases. ESS identified pathogenic somatic variants in 73% (11/15 cases) versus 53% (8/15 cases) using TS. Clinically focused ESS identified 63 variants, consisting of 30 somatic variants (including all 13 identified by TS) and 33 germline variants. Overall, there were 48 unique variants. ESS had a clinical impact in 53% (8/15 cases); 47% (7/15 cases) were referred to the familial cancer clinic, and 'druggable' targets were identified in 53% (8/15 cases).

CONCLUSION

ESS of tumour DNA impacted clinical decision-making in 53%, with 20% more pathogenic variants identified through ESS than TS. The identification of germline variants in 47% was an unexpected finding.

Differences in overall survival and mutation prevalence between right- and left-sided colorectal adenocarcinoma

Background

Prior reports have demonstrated inferior outcomes for patients with right-sided colorectal cancer (CRC) compared to patients with left-sided disease, as well as differences in treatment response based on disease sidedness. Differences in prognosis remain even among patients with metastatic disease, indicating that anatomy or stage at diagnosis alone cannot explain all of these findings. While genetic differences between right- and left-sided CRC have long been described, the genetic and molecular drivers underlying differences in prognosis and treatment response remain incompletely understood.

Methods

We compared mutation prevalence between right- (cecum to splenic flexure) and left-sided (descending colon to rectum) CRC among 38 genes in a retrospective review of next-generation sequencing data of CRC samples obtained in routine clinical practice at a single academic medical center.

Results

Among 288 cases (167 left-sided, 103 right-sided, 18 synchronous or without clear primary), patients with left-sided primaries had a longer overall survival from pathologic diagnosis (median 1,823 days vs. 1,006 days for right-sided cases, P=0.004). Among the assessed genes, BRAF and CTNNB1 mutations were more prevalent in right-sided CRC. BRAF was mutated in 15.5% of right-sided CRC (95% CI: 8.5-22.5%) compared to 4.8% (95% CI: 1.6-8.0%) (P=0.003). CTNNB1 was mutated in 3.9% of right-sided CRC (95% CI: 0.2-7.6%) compared to no instances of CTNNB1 mutations in left-sided disease (P=0.01).

Conclusions

This difference in mutation prevalence may implicate these genetic pathways in the mechanisms underlying the discrepant outcomes and treatment responses between right- and left-sided CRC described in this and prior studies.