Emerging biomarkers and targeted therapies in urothelial carcinoma

The use of immunotherapy has revolutionized the management of patients with locally advanced, unresectable, and metastatic urothelial carcinoma (UC); however, platinum-based chemotherapy remains a therapeutic cornerstone both in localized muscle-invasive and advanced UC. There is still no predictive molecular biomarker with clinical utility to help guide treatment and select patients most likely to derive benefit from a particular therapeutic modality or regimen. However, recent research has further characterized the inherent biology and immunology landscapes of UC leading to the development of potential biomarkers and therapeutic targets that could be used upon further validation. Emerging interrogation of The Cancer Genome Atlas (TCGA) and other molecular profiling datasets has led to the identification of distinct molecular subtypes with diverse clinical behaviors with potential sensitivity to various therapies. It has also led to the discovery of multiple frequently altered genes and proteins that could lead to perturbation of intracellular signaling pathways and of the dynamic interactions between tumor cells, their "microenvironment", and the host "macro-environment". The advent of molecular profiling and deeper next-generation sequencing has the potential to change biomarker and "real time" drug sensitivity assessment, introducing and testing the premise of "precision oncology" and personalized medicine. Within this review, we summarize emerging biomarkers that may predict response to cisplatin-based chemotherapy, immunotherapy, emerging targeted therapies, and promising combination strategies. We also highlight a few examples of 'precision medicine' trials aiming to improve outcomes in UC. Since our review is not exhaustive we strongly recommend the readers to follow the continuously changing literature in the very interesting and dynamic field of UC.

Emerging roles for clinical pharmacometrics in cancer precision medicine

Purpose of review

Although significant progress has been made in cancer research, there exist unmet needs in patient care as reflected by the 'Cancer Moonshot' goals. This review appreciates the potential utility of quantitative pharmacology in cancer precision medicine.

Recent findings

Precision oncology has received federal funding largely due to 'The Precision Medicine Initiative'. Precision medicine takes into account the inter-individual variability, and allows for tailoring the right medication or the right dose of drug to the best subpopulation of patients who will likely respond to the intervention, thus enhancing therapeutic success and reducing "financial toxicity" to patients, families and caregivers. The National Cancer Institute (NCI) committed US$ 70 million from its fiscal year 2016 budget to advance precision oncology research. Through the 'Critical Path Initiative', pharmacometrics has gained an important role in drug development; however, it is yet to find widespread clinical applicability.

Summary

Stakeholders including clinicians and pharmacometricians need to work in concert to ensure that benefits of model-based approaches are harnessed to personalize cancer care to the individual needs of the patient via better dosing strategies, companion diagnostics, and predictive biomarkers. In medical oncology, where immediate patient care is the clinician's primary concern, pharmacometric approaches can be tailored to build models that rely on patient data already digitally available in the Electronic Health Record (EHR) to facilitate quick collaboration and avoid additional funding needs. Taken together, we offer a roadmap for the future of precision oncology which is fraught with both challenges and opportunities for pharmacometricians and clinicians alike.

Oncogenic Signaling Pathways in The Cancer Genome Atlas

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.

Value-based genomics

Advancements in next-generation sequencing have greatly enhanced the development of biomarker-driven cancer therapies. The affordability and availability of next-generation sequencers have allowed for the commercialization of next-generation sequencing platforms that have found widespread use for clinical-decision making and research purposes. Despite the greater availability of tumor molecular profiling by next-generation sequencing at our doorsteps, the achievement of value-based care, or improving patient outcomes while reducing overall costs or risks, in the era of precision oncology remains a looming challenge. In this review, we highlight available data through a pre-established and conceptualized framework for evaluating value-based medicine to assess the cost (efficiency), clinical benefit (effectiveness), and toxicity (safety) of genomic profiling in cancer care. We also provide perspectives on future directions of next-generation sequencing from targeted panels to whole-exome or whole-genome sequencing and describe potential strategies needed to attain value-based genomics.

Evolving Roles of Histologic Evaluation and Molecular/Genomic Profiling in the Management of Endometrial Cancer

Endometrial cancers are the most common gynecologic malignancies. The staging of endometrial cancer has evolved from a clinical-based system to a comprehensive surgical-pathologic approach that allows for better risk stratification and treatment planning. Over the past few years, use of NCCN's sentinel lymph node (SLN) mapping algorithm for the surgical staging of endometrial cancer has gained significant acceptance and is now commonly applied in many practices. However, pathologic evaluation of prognostic factors is beset by challenges, including the reproducibility of histologic classification and FIGO's grading, as well as the questionable clinical significance of low-volume tumor in SLNs. With the revelation of major genomic classes of endometrial cancer comes the potential for improved, reproducible, and prognostically relevant classification schemes, which integrate traditional pathologic parameters with genomic findings, to aid in treatment decisions. Pathologic identification of new variants of endometrial cancer, such as undifferentiated carcinoma, continues to advance the phenotypic spectrum of these tumors, spurring genomic and functional studies to further characterize their mechanistic underpinnings and potentially reveal new avenues for treatment. In the era of precision medicine, pathologic assessment of biomarkers (eg, mismatch repair proteins) and recognition of phenotypes that are amenable to specific targeted therapies (such as POLE-mutated tumors) have become integral to the management of women with endometrial carcinoma.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Challenging Standard-of-Care Paradigms in the Precision Oncology Era

The pace of genomic and immunological breakthroughs in oncology is accelerating, making it likely that large randomized trials will increasingly become outdated before their completion. Traditional clinical research/practice paradigms must adapt to the reality unveiled by genomics, especially the need for customized drug combinations, rather than one-size-fits-all monotherapy. The raison-d'être of precision oncology is to offer 'the right drug for the right patient at the right time', a process enabled by transformative tissue and blood-based genomic technologies. Genomically targeted therapies are most suitable in early disease, when molecular heterogeneity is less pronounced, while immunotherapy is most effective against tumors with unstable genomes. Next-generation cancer research/practice models will need to overcome the tyranny of tradition and emphasize an innovative, precise and personalized patient-centric approach.

Emergence of Intrahepatic Cholangiocarcinoma: How High-Throughput Technologies Expedite the Solutions for a Rare Cancer Type

Intrahepatic cholangiocarcinoma (ICC) is the cancer of the intrahepatic bile ducts, and together with hepatocellular carcinoma (HCC), constitute the majority of primary liver cancers. ICC is a rare disorder as its overall incidence is < 1/100,000 in the United States and Europe. However, it shows much higher incidence in particular geographical regions, such as northeastern Thailand, where liver fluke infection is the most common risk factor of ICC. Since the early stages of ICC are often asymptomatic, the patients are usually diagnosed at advanced stages with no effective treatments available, leading to the high mortality rate. In addition, unclear genetic mechanisms, heterogeneous nature, and various etiologies complicate the development of new efficient treatments. Recently, a number of studies have employed high-throughput approaches, including next-generation sequencing and mass spectrometry, in order to understand ICC in different biological aspects. In general, the majority of recurrent genetic alterations identified in ICC are enriched in known tumor suppressor genes and oncogenes, such as mutations in TP53, KRAS, BAP1, ARID1A, IDH1, IDH2, and novel FGFR2 fusion genes. Yet, there are no major driver genes with immediate clinical solutions characterized. Interestingly, recent studies utilized multi-omics data to classify ICC into two main subgroups, one with immune response genes as the main driving factor, while another is enriched with driver mutations in the genes associated with epigenetic regulations, such as IDH1 and IDH2. The two subgroups also show different hypermethylation patterns in the promoter regions. Additionally, the immune response induced by host-pathogen interactions, i.e., liver fluke infection, may further stimulate tumor growth through alterations of the tumor microenvironment. For in-depth functional studies, although many ICC cell lines have been globally established, these homogeneous cell lines may not fully explain the highly heterogeneous genetic contents of this disorder. Therefore, the advent of patient-derived xenograft and 3D patient-derived organoids as new disease models together with the understanding of evolution and genetic alterations of tumor cells at the single-cell resolution will likely become the main focus to fill the current translational research gaps of ICC in the future.

Potential for subsets of wt-NPM1 primary AML blasts to respond to retinoic acid treatment

Acute myeloid leukemia (AML) has high mortality rates, perhaps reflecting a lack of understanding of the molecular diversity in various subtypes and a lack of known actionable targets. There are currently 12 open clinical trials for AML using combination therapeutic modalities including all-trans retinoic acid (RA). Mutant nucleophosmin-1, proposed as a possible marker for RA response, is the criterion for recruiting patients in three active RA phase 3 clinical trials. We tested the ability of RA alone or in combination with either bosutinib (B) or 6-formylindolo(3,2-b) carbazole (F) to induce conversion of 12 de novo AML samples toward a more differentiated phenotype. We assessed levels of expression of cell surface markers associated with differentiation, aldehyde dehydrogenase activity, and glucose uptake activity. Colony formation capacity was reduced with the combined treatment of RA and B or F, and correlated with modulation of a c-Cbl/Lyn/c-Raf-centered signalsome. Combination treatment was in most cases more effective than RA alone. Based on their responses to the treatments, some primary leukemic samples cluster closer to HL-60 cells than to other primary samples, suggesting that they may represent a hitherto undefined AML subtype that is potentially responsive to RA in a combination differentiation therapy.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

Fishing for cures: The alLURE of using zebrafish to develop precision oncology therapies

Zebrafish have proven to be a valuable model to study human cancer biology with the ultimate aim of developing new therapies. Danio rerio are amenable to in vivo imaging, high-throughput drug screening, mutagenesis, and transgenesis, and they share histological and genetic similarities with Homo sapiens. The significance of zebrafish in the field of precision oncology is rapidly emerging. Indeed, modeling cancer in zebrafish has already been used to identify tumor biomarkers, define therapeutic targets and provide an in vivo platform for drug discovery. New zebrafish studies are starting to pave the way to direct individualized clinical applications. Patient-derived cancer cell xenograft models have demonstrated the feasibility of using zebrafish as a real-time avatar of prognosis and drug response to identify the most ideal therapy for an individual patient. Genetic cancer modeling in zebrafish, now facilitated by rapidly evolving genome editing techniques, represents another innovative approach to recapitulate human oncogenesis and develop individualized treatments. Utilizing zebrafish to design customizable precision therapies will improve the clinical outcome of patients afflicted with cancer.

Precision oncology using a clinician-directed, tailored approach to molecular profiling

AIM

Precision oncology involves molecularly matching patients to targeted agents usually in early drug development (EDD) programs. Molecular profiling (MP) identifies actionable targets. Comprehensive commercial MP platforms are costly and in resource limited environments, a more practical approach to MP is necessary to support EDD and precision oncology. We adopted a clinician-directed, tailored approach to MP to enrol patients onto molecularly targeted trials. We report the feasibility of this approach.

METHODS

All patients referred to the Royal Melbourne Hospital (RMH) EDD between September 2013 and September 2015 were identified in a prospective database. Key captured data included clinicopathological data, MP platform ordered (if any), molecular targets identified and subsequent enrolment onto clinical trials. EDD-clinician decisions to order MP and the platform utilized was guided by patient consultation, tumor type, trial availability and requirement for molecular information.

RESULTS

We identified 377 patients referred to RMH EDD. A total of 216 (57%) had MP ordered. The remainder had known actionable targets (19%), or were inappropriate for clinical trials (24%). In those undergoing MP, 187 genetic aberrations were found in 113 patients with 98 considered actionable targets in 86 patients. Ninety-eight (25%) patients were enrolled onto a clinical trial, including 40 (11%) receiving molecularly matched treatments. Median progression-free survival was improved in patients enrolled onto molecularly matched trials compared to those on unmatched trials (3.6 months vs 1.9 months, HR 0.58 [0.38-0.89], P  =  0.013).

CONCLUSION

A clinician-directed, tailored approach to the use of MP is feasible, resulting in 11% of patients enrolled onto molecularly matched trials.

Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p < 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization.

Precision oncology: neither a silver bullet nor a dream

Precision oncology is not an illusion, nor is it the magic bullet that will eradicate all cancers. Precision oncology is simply another weapon in our growing armament against cancer. Rather than honing in on the failures of a relatively young field, one should advocate for integrating its successes into widespread clinical practice, especially for indications, such as: ABL, ALK, BRAF, BRCA1, BRCA2, EGFR, KIT, KRAS, PDGFRA, PDGFRB, ROS1, BCR-ABL, FLT3 and ROS1, where aberrations have been shown to alter responses to US FDA approved drugs - that is, level 1 data. Moreover, to truly assess the promise of precision oncology, we must first begin by defining our expectations for this field. Importantly, we must recognize that the conception of precision oncology arose as an antithesis of the 'one-size fits all' cancer therapeutics approach. Consequently, tools used for evaluating these conventional, large-scale trials, are not directly transferable for assessing nonconventional, smaller-scale trials needed for evaluating precision oncology. Hence, a thorough vetting of precision oncology as another tool of the trade, must first begin by reassessing our expectations for this field, as well as current clinical trial designs and end point measurements. Importantly, we must recognize that most targeted therapy approaches are in their infancy, with only monotherapy approaches being assessed and combination therapies likely being necessary to fulfill the promise of precision oncology.

Value of a molecular screening program to support clinical trial enrollment in Asian cancer patients: The Integrated Molecular Analysis of Cancer (IMAC) Study

The value of precision oncology initiatives in Asian contexts remains unresolved. Here, we review the institutional implementation of prospective molecular screening to facilitate accrual of patients into biomarker-driven clinical trials, and to explore the mutational landscape of advanced tumors occurring in a prospective cohort of Asian patients (n = 396) with diverse cancer types. Next-generation sequencing (NGS) and routine clinicopathological assays, such as immunohistochemistry, copy number analysis and in situ hybridization tests, were performed on tumor samples. Actionable biomarker results were used to identify eligibility for early-phase, biomarker-driven clinical trials. Overall, NGS was successful in 365 of 396 patients (92%), achieving a mean depth of 1,943× and coverage uniformity of 96%. The median turnaround time from sample receipt to return of genomic results was 26.0 days (IQR, 19.0-39.0 days). Reportable mutations were found in 300 of 365 patients (82%). Ninety-one percent of patients at study enrollment indicated consent to receive incidental findings and willingness to undergo genetic counseling if required. The most commonly mutated oncogenes included KRAS (19%), PIK3CA (16%), EGFR (5%), BRAF (3%) and KIT (3%); while the most frequently mutated tumor suppressor genes included TP53 (40%), SMARCB1 (12%), APC (8%), PTEN (6%) and SMAD4 (5%). Among 23 patients enrolled in genotype-matched trials, median progression-free survival was 2.9 months (IQR, 1.5-4.0 months). Nine of 20 evaluable patients (45%; 95% CI, 23.1-68.5%) derived clinical benefit, including 3 partial responses and 6 with stable disease lasting ≥ 8 weeks.

Resources for Interpreting Variants in Precision Genomic Oncology Applications

Precision genomic oncology-applying high throughput sequencing (HTS) at the point-of-care to inform clinical decisions-is a developing precision medicine paradigm that is seeing increasing adoption. Simultaneously, new developments in targeted agents and immunotherapy, when informed by rich genomic characterization, offer potential benefit to a growing subset of patients. Multiple previous studies have commented on methods for identifying both germline and somatic variants. However, interpreting individual variants remains a significant challenge, relying in large part on the integration of observed variants with biological knowledge. A number of data and software resources have been developed to assist in interpreting observed variants, determining their potential clinical actionability, and augmenting them with ancillary information that can inform clinical decisions and even generate new hypotheses for exploration in the laboratory. Here, we review available variant catalogs, variant and functional annotation software and tools, and databases of clinically actionable variants that can be used in an ad hoc approach with research samples or incorporated into a data platform for interpreting and formally reporting clinical results.

A bioinformatics-to-clinic sequential approach to analysis of prostate cancer biomarkers using TCGA datasets and clinical samples: a new method for precision oncology?

Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. To identify target molecules implicated in shorter survival of patients with PACa, we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step in silico analysis of a TCGA dataset for localized PACa. The effect of candidate genes identified by in silico analysis on survival was then assessed using biopsy specimens taken at the time of initial diagnosis of localized and metastatic PACa. We identified PEG10 as a candidate biomarker. Data from clinical samples suggested that increased expression of PEG10 at the time of initial diagnosis was linked to shorter survival time. Interestingly, PEG10 overexpression also correlated with expression of chromogranin A and synaptophysin, markers for neuroendocrine prostate cancer, a type of treatment-resistant prostate cancer. These results indicate that PEG10 is a novel biomarker for shorter survival of patients with PACa. Also, PEG10 expression at the time of initial diagnosis may predict focal neuroendocrine differentiation of PACa. Thus, PEG10 may be an attractive target for biomarker-driven cancer therapy. Thus, bioinformatics-to-clinic sequential analysis is a valid tool for identifying targets for precision oncology.

Integrative analysis of novel hypomethylation and gene expression signatures in glioblastomas

Molecular and clinical heterogeneity critically hinders better treatment outcome for glioblastomas (GBMs); integrative analysis of genomic and epigenomic data may provide useful information for improving personalized medicine. By applying training-validation approach, we identified a novel hypomethylation signature comprising of three CpGs at non-CpG island (CGI) open sea regions for GBMs. The hypomethylation signature consistently predicted poor prognosis of GBMs in a series of discovery and validation datasets. It was demonstrated as an independent prognostic indicator, and showed interrelationships with known molecular marks such as MGMT promoter methylation status, and glioma CpG island methylator phenotype (G-CIMP) or IDH1 mutations. Bioinformatic analysis found that the hypomethylation signature was closely associated with the transcriptional status of an EGFR/VEGFA/ANXA1-centered gene network. The integrative molecular analysis finally revealed that the gene network defined two distinct clinically relevant molecular subtypes reminiscent of different immature neuroglial lineages in GBMs. The novel hypomethylation signature and relevant gene network may provide new insights into prognostic classification, molecular characterization, and treatment development for GBMs.

Next generation sequencing of pancreatic ductal adenocarcinoma: right or wrong?

Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate of all epithelial malignancies and a paradoxically rising incidence rate. Clinical translation of next generation sequencing (NGS) of tumour and germline samples may ameliorate outcomes by identifying prognostic and predictive genomic and transcriptomic features in appreciable fractions of patients, facilitating enrolment in biomarker-matched trials. Areas covered: The literature on precision oncology is reviewed. It is found that outcomes may be improved across various malignancies, and it is suggested that current issues of adequate tissue acquisition, turnaround times, analytic expertise and clinical trial accessibility may lessen as experience accrues. Also reviewed are PDAC genomic and transcriptomic NGS studies, emphasizing discoveries of promising biomarkers, though these require validation, and the fraction of patients that will benefit from these outside of the research setting is currently unknown. Expert commentary: Clinical use of NGS with PDAC should be used in investigational contexts in centers with multidisciplinary expertise in cancer sequencing and pancreatic cancer management. Biomarker directed studies will improve our understanding of actionable genomic variation in PDAC, and improve outcomes for this challenging disease.

Precision oncology based on omics data: The NCT Heidelberg experience

Precision oncology implies the ability to predict which patients will likely respond to specific cancer therapies based on increasingly accurate, high-resolution molecular diagnostics as well as the functional and mechanistic understanding of individual tumors. While molecular stratification of patients can be achieved through different means, a promising approach is next-generation sequencing of tumor DNA and RNA, which can reveal genomic alterations that have immediate clinical implications. Furthermore, certain genetic alterations are shared across multiple histologic entities, raising the fundamental question of whether tumors should be treated by molecular profile and not tissue of origin. We here describe MASTER (Molecularly Aided Stratification for Tumor Eradication Research), a clinically applicable platform for prospective, biology-driven stratification of younger adults with advanced-stage cancer across all histologies and patients with rare tumors. We illustrate how a standardized workflow for selection and consenting of patients, sample processing, whole-exome/genome and RNA sequencing, bioinformatic analysis, rigorous validation of potentially actionable findings, and data evaluation by a dedicated molecular tumor board enables categorization of patients into different intervention baskets and formulation of evidence-based recommendations for clinical management. Critical next steps will be to increase the number of patients that can be offered comprehensive molecular analysis through collaborations and partnering, to explore ways in which additional technologies can aid in patient stratification and individualization of treatment, to stimulate clinically guided exploratory research projects, and to gradually move away from assessing the therapeutic activity of targeted interventions on a case-by-case basis toward controlled clinical trials of genomics-guided treatments.

Functional lung avoidance and response-adaptive escalation (FLARE) RT: Multimodality plan dosimetry of a precision radiation oncology strategy

PURPOSE

Nonsmall cell lung cancer (NSCLC) patient radiation therapy (RT) is planned without consideration of spatial heterogeneity in lung function or tumor response. We assessed the dosimetric and clinical feasibility of functional lung avoidance and response-adaptive escalation (FLARE) RT to reduce dose to [^99m Tc]MAA-SPECT/CT perfused lung while redistributing an escalated boost dose within [¹⁸ F]FDG-PET/CT-defined biological target volumes (BTV).

METHODS

Eight stage IIB-IIIB NSCLC patients underwent FDG-PET/CT and MAA-SPECT/CT treatment planning scans. Perfused lung objectives were derived from scatter/collimator/attenuation-corrected MAA-SPECT uptake relative to ITV-subtracted lung to maintain < 20 Gy mean lung dose (MLD). Prescriptions included 60 Gy to the planning target volume (PTV) and concomitant boost of 74 Gy mean to biological target volumes (BTV = GTV + PET gradient segmentation) scaled to each BTV voxel by relative FDG-PET SUV. Dose-painting-by-numbers prescriptions were integrated into commercial treatment planning systems via uptake threshold discretization. Dose constraints for lung, heart, cord, and esophagus were defined. FLARE RT plans were optimized with volumetric modulated arc therapy (VMAT), proton pencil beam scanning (PBS) with 3%-3 mm robust optimization, and combination of PBS (avoidance) plus VMAT (escalation). The high boost dose region was evaluated within a standardized SUV_peak structure. FLARE RT plans were compared to reference VMAT plans. Linear regression between radiation dose to BTV and normalized FDG PET SUV at every voxel was conducted, from which Pearson linear correlation coefficients and regression slopes were extracted. Spearman rank correlation coefficients were estimated between radiation dose to lung and normalized SPECT uptake. Dosimetric differences between treatment modalities were evaluated by Friedman nonparametric paired test with multiple sampling correction.

RESULTS

No unacceptable violations of PTV and normal tissue objectives were observed in 24 FLARE RT plans. Compared to reference VMAT plans, FLARE VMAT plans achieved a higher mean dose to BTV (73.7 Gy 98195. 61.3 Gy), higher mean dose to SUV_peak (89.7 Gy vs. 60.8 Gy), and lower mean dose to highly perfused lung (7.3 Gy vs. 14.9 Gy). These dosimetric gains came at the expense of higher mean heart dose (9.4 Gy vs. 5.8 Gy) and higher maximum cord dose (50.1 Gy vs. 44.6 Gy) relative to the reference VMAT plans. Between FLARE plans, FLARE VMAT achieved higher dose to the SUV_peak ROI than FLARE PBS (89.7 Gy vs. 79.2 Gy, P = 0.01), while FLARE PBS delivered lower dose to lung than FLARE VMAT (11.9 Gy vs. 15.6 Gy, P < 0.001). Voxelwise linear dose redistribution slope between BTV dose and FDG PET uptake was higher in magnitude for FLARE PBS + VMAT (0.36 Gy per %SUV_max ) compared to FLARE VMAT (0.27 Gy per %SUV_max ) or FLARE PBS alone (0.17 Gy per %SUV_max ).

CONCLUSIONS

FLARE RT is clinically feasible with VMAT and PBS. A combination of PBS for functional lung avoidance and VMAT for FDG PET dose escalation balanced target and normal tissue objective tradeoffs. These results provide a technical platform for testing of FLARE RT safety and efficacy within a precision radiation oncology trial.

Clinical application of a cancer genomic profiling assay to guide precision medicine decisions

Aim:

Develop and apply a comprehensive and accurate next-generation sequencing based assay to help clinicians to match oncology patients to therapies.

Materials & methods:

The performance of the CANCERPLEX^® assay was assessed using DNA from well-characterized routine clinical formalin-fixed paraffin-embedded (FFPE) specimens and cell lines.

Results:

The maximum sensitivity of the assay is 99.5% and its accuracy is virtually 100% for detecting somatic alterations with an allele fraction of as low as 10%. Clinically actionable variants were identified in 93% of patients (930 of 1000) who underwent testing.

Conclusion:

The test's capacity to determine all of the critical genetic changes, tumor mutation burden, microsatellite instability status and viral associations has important ramifications on clinical decision support strategies, including identification of patients who are likely to benefit from immune checkpoint blockage therapies.

Harnessing Solute Carrier Transporters for Precision Oncology

Solute Carrier (SLC) transporters are a large superfamily of transmembrane carriers involved in the regulated transport of metabolites, nutrients, ions and drugs across cellular membranes. A subset of these solute carriers play a significant role in the cellular uptake of many cancer therapeutics, ranging from chemotherapeutics such as antimetabolites, topoisomerase inhibitors, platinum-based drugs and taxanes to targeted therapies such as tyrosine kinase inhibitors. SLC transporters are co-expressed in groups and patterns across normal tissues, suggesting they may comprise a coordinated regulatory circuit serving to mediate normal tissue functions. In cancer however, there are dramatic changes in expression patterns of SLC transporters. This frequently serves to feed the increased metabolic demands of the tumor cell for amino acids, nucleotides and other metabolites, but also presents a therapeutic opportunity, as increased transporter expression may serve to increase intracellular concentrations of substrate drugs. In this review, we examine the regulation of drug transporters in cancer and how this impacts therapy response, and discuss novel approaches to targeting therapies to specific cancers via tumor-specific aberrations in transporter expression. We propose that among the oncogenic changes in SLC transporter expression there exist emergent vulnerabilities that can be exploited therapeutically, extending the application of precision medicine from tumor-specific drug targets to tumor-specific determinants of drug uptake.