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

The Clinical Prediction Value of the Ubiquitination Model Reflecting the Immune Traits in LUAD

Background

Increasing evidence shows that the ubiquitin–proteasome system has a crucial impact on lung adenocarcinoma. However, reliable prognostic signatures based on ubiquitination and immune traits have not yet been established.

Methods

Bioinformatics was performed to analyze the characteristic of ubiquitination in lung adenocarcinoma. Principal component analysis was employed to identify the difference between lung adenocarcinoma and adjacent tissue. The ubiquitin prognostic risk model was constructed by multivariate Cox regression and least absolute shrinkage and selection operator regression based on the public database The Cancer Genome Atlas, with evaluation of the time-dependent receiver operating characteristic curve. A variety of algorithms was used to analyze the immune traits of model stratification. Meanwhile, the drug response sensitivity for subgroups was predicted by the “pRRophetic” package based on the database of the Cancer Genome Project.

Results

The expression of ubiquitin genes was different in the tumor and in the adjacent tissue. The ubiquitin model was superior to the clinical indexes, and four validation datasets verified the prognostic effect. Additionally, the stratification of the model reflected distinct immune landscapes and mutation traits. The low-risk group was infiltrating plenty of immune cells and highly expressed major histocompatibility complex and immune genes, which illustrated that these patients could benefit from immune treatment. The high-risk group showed higher mutation and tumor mutation burden. Integrating the tumor mutation burden and the immune score revealed the patient’s discrepancy between survival and drug response. Finally, we discovered that the drug targeting ubiquitin and proteasome would be a beneficial prospective treatment for lung adenocarcinoma.

Conclusion

The ubiquitin trait could reflect the prognosis of lung adenocarcinoma, and it might shed light on the development of novel ubiquitin biomarkers and targeted therapy for lung adenocarcinoma.

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All

Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets.

Contribution of Epithelial and Gut Microbiome Inflammatory Biomarkers to the Improvement of Colorectal Cancer Patients' Stratification

In order to ensure that primary endpoints of clinical studies are attained, the patients' stratification is an important aspect. Selection criteria include age, gender, and also specific biomarkers, such as inflammation scores. These criteria are not sufficient to achieve a straightforward selection, however, in case of multifactorial diseases, with unknown or partially identified mechanisms, occasionally including host factors, and the microbiome. In these cases, the efficacy of interventions is difficult to predict, and as a result, the selection of subjects is often random. Colorectal cancer (CRC) is a highly heterogeneous disease, with variable clinical features, outcomes, and response to therapy; the CRC onset and progress involves multiple sequential steps with accumulation of genetic alterations, namely, mutations, gene amplification, and epigenetic changes. The gut microbes, either eubiotic or dysbiotic, could influence the CRC evolution through a complex and versatile crosstalk with the intestinal and immune cells, permanently changing the tumor microenvironment. There have been significant advances in the development of personalized approaches for CRC screening, treatment, and potential prevention. Advances in molecular techniques bring new criteria for patients' stratification-mutational analysis at the time of diagnosis to guide treatment, for example. Gut microbiome has emerged as the main trigger of gut mucosal homeostasis. This may impact cancer susceptibility through maintenance of the epithelial/mucus barrier and production of protective metabolites, such as short-chain fatty acids (SCFAs) via interactions with the hosts' diet and metabolism. Microbiome dysbiosis leads to the enrichment of cancer-promoting bacterial populations, loss of protective populations or maintaining an inflammatory chronic state, all of which contribute to the development and progression of CRC. Meanwhile, variations in patient responses to anti-cancer immuno- and chemotherapies were also linked to inter-individual differences in intestine microbiomes. The authors aim to highlight the contribution of epithelial and gut microbiome inflammatory biomarkers in the improvement of CRC patients' stratification towards a personalized approach of early diagnosis and treatment.

Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

Genome- and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution, but its characteristics and implication in cancer evolution and therapy remain largely unexplored. Here, we analyzed large-scale transcriptome/proteome profiles, such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and found that compared to normal tissues, different cancer types showed a convergent pattern toward using biosynthetically low-cost amino acids. Such a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids, termed energy cost per amino acid (ECPA). With this index, we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development, and revealed their reversed patterns. Finally, focusing on the liver, a tissue with a dramatic increase in ECPA during development, we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single genes. Our study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

Recent Major Transcriptomics and Epitranscriptomics Contributions toward Personalized and Precision Medicine

With the advent of genome-wide screening methods-beginning with microarray technologies and moving onto next generation sequencing methods-the era of precision and personalized medicine was born. Genomics led the way, and its contributions are well recognized. However, "other-omics" fields have rapidly emerged and are becoming as important toward defining disease causes and exploring therapeutic benefits. In this review, we focus on the impacts of transcriptomics, and its extension-epitranscriptomics-on personalized and precision medicine efforts. There has been an explosion of transcriptomic studies particularly in the last decade, along with a growing number of recent epitranscriptomic studies in several disease areas. Here, we summarize and overview major efforts for cancer, cardiovascular disease, and neurodevelopmental disorders (including autism spectrum disorder and intellectual disability) for transcriptomics/epitranscriptomics in precision and personalized medicine. We show that leading advances are being made in both diagnostics, and in investigative and landscaping disease pathophysiological studies. As transcriptomics/epitranscriptomics screens become more widespread, it is certain that they will yield vital and transformative precision and personalized medicine contributions in ways that will significantly further genomics gains.

Functional Precision Medicine Provides Clinical Benefit in Advanced Aggressive Hematologic Cancers and Identifies Exceptional Responders

Personalized medicine aims to match the right drug with the right patient by using specific features of the individual patient's tumor. However, current strategies of personalized therapy matching provide treatment opportunities for less than 10% of patients with cancer. A promising method may be drug profiling of patient biopsy specimens with single-cell resolution to directly quantify drug effects. We prospectively tested an image-based single-cell functional precision medicine (scFPM) approach to guide treatments in 143 patients with advanced aggressive hematologic cancers. Fifty-six patients (39%) were treated according to scFPM results. At a median follow-up of 23.9 months, 30 patients (54%) demonstrated a clinical benefit of more than 1.3-fold enhanced progression-free survival compared with their previous therapy. Twelve patients (40% of responders) experienced exceptional responses lasting three times longer than expected for their respective disease. We conclude that therapy matching by scFPM is clinically feasible and effective in advanced aggressive hematologic cancers. SIGNIFICANCE: This is the first precision medicine trial using a functional assay to instruct n-of-one therapies in oncology. It illustrates that for patients lacking standard therapies, high-content assay-based scFPM can have a significant value in clinical therapy guidance based on functional dependencies of each patient's cancer.See related commentary by Letai, p. 290.This article is highlighted in the In This Issue feature, p. 275.

Association of differential expression of immunoregulatory molecules and presence of targetable mutations may inform rational design of clinical trials

Background

Immune checkpoint inhibitors (ICIs) and genomic biomarker-driven targeted therapies have revolutionized the modern oncologic treatment arsenal. The next step has been to combine targeted agents and ICIs. In doing so, some combination regimens may be more logical than others.

Patients and methods

Whole-exome and whole-transcriptome sequencing were performed on 2739 unselected later-stage clinical cases from 24 solid tumor subtypes in the NantHealth database, and data were also curated from 5746 similarly sequenced patients across 28 solid tumor subtypes in The Cancer Genome Atlas (TCGA). Significant differential expression of 10 immunoregulatory molecules [IRMs (genes)] was analyzed for association with mutant versus wild-type genes.

Results

Twenty-three significant associations between currently actionable variants and RNA-expressed checkpoint genes were identified in the TCGA cases; 10 were validated in the external cohort of 2739 clinical cases from NantHealth (P values were adjusted using Benjamini–Hochberg multiple hypothesis correction to reduce false-discovery rate). Within the same 5746 TCGA profiles, 2740 TCGA patients were identified as having one or more potentially oncogenic single-nucleotide variant (SNV) mutation within an established 50-gene hotspot panel. Of the 50 genes, SNVs within 15 were found to be significantly associated with differential expression of at least one IRM after adjusting for tissue enrichment; six were confirmed significant associations in an independent set of 2739 clinical cases from NantHealth.

Conclusions

Logically combining ICIs with targeted therapies may offer unique treatment strategies for patients with cancer. The presence of specific mutations impacts the expression of IRMs, an observation of potential importance for selecting combinations of gene- and immune-targeted therapeutics.

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Altered actionable genes correlated with specific checkpoint transcripts.

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Associations between IRMs and altered genes were validated in independent datasets.

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Combining immune- and gene-targeted drugs based on IRM/gene correlations merits study.

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels

Transcriptomic profiling is critical to uncovering functional elements from transcriptional and post-transcriptional aspects. Here, we present Gene Expression Nebulas (GEN, https://ngdc.cncb.ac.cn/gen/), an open-access data portal integrating transcriptomic profiles under various biological contexts. GEN features a curated collection of high-quality bulk and single-cell RNA sequencing datasets by using standardized data processing pipelines and a structured curation model. Currently, GEN houses a large number of gene expression profiles from 323 datasets (157 bulk and 166 single-cell), covering 50 500 samples and 15 540 169 cells across 30 species, which are further categorized into six biological contexts. Moreover, GEN integrates a full range of transcriptomic profiles on expression, RNA editing and alternative splicing for 10 bulk datasets, providing opportunities for users to conduct integrative analysis at both transcriptional and post-transcriptional levels. In addition, GEN provides abundant gene annotations based on value-added curation of transcriptomic profiles and delivers online services for data analysis and visualization. Collectively, GEN presents a comprehensive collection of transcriptomic profiles across multiple species, thus serving as a fundamental resource for better understanding genetic regulatory architecture and functional mechanisms from tissues to cells.

Real-World Data From a Molecular Tumor Board: Improved Outcomes in Breast and Gynecologic Cancers Patients With Precision Medicine

PURPOSE

Next-generation sequencing is increasingly used in gynecologic and breast cancers. Multidisciplinary Molecular Tumor Board (MTB) may guide matched therapy; however, outcome data are limited. We evaluate the effect of the degree of matching of tumors to treatment as well as compliance to MTB recommendations on outcomes.

METHODS

Overall, 164 patients with consecutive gynecologic and breast cancers presented at MTB were assessed for clinicopathologic data, next-generation sequencing results, MTB recommendations, therapy received, and outcomes. Matching score (MS), defined as percentage of alterations targeted by treatment over total pathogenic alterations, and compliance to MTB recommendations were analyzed in context of oncologic outcomes.

RESULTS

Altogether, 113 women were evaluable for treatment after MTB; 54% received matched therapy. Patients with MS ≥ 40% had higher overall response rate (30.8% v 7.1%; P = .001), progression-free survival (PFS; hazard ratio [HR] 0.51; 95% CI, 0.31 to 0.85; P = .002), and a trend toward improved overall survival (HR 0.64; 95% CI, 0.34 to 1.25; P = .082) in univariate analysis. The PFS advantage remained significant in multivariate analysis (HR 0.5; 95% CI, 0.3 to 0.8; P = .006). Higher MTB recommendation compliance was significantly associated with improved median PFS (9.0 months for complete; 6.0 months for partial; 4.0 months for no compliance; P = .004) and overall survival (17.1 months complete; 17.8 months partial; 10.8 months none; P = .046). Completely MTB-compliant patients had higher MS (P < .001). In multivariate analysis comparing all versus none MTB compliance, overall response (HR 9.5; 95% CI, 2.6 to 35.0; P = .001) and clinical benefit (HR 8.8; 95% CI, 2.4 to 33.2; P = .001) rates were significantly improved with higher compliance.

CONCLUSION

Compliance to MTB recommendations resulted in higher degrees of matched therapy and correlates with improved outcomes in patients with gynecologic and breast cancers.

OUP accepted manuscript

Background

Cancer of unknown primary (CUP) comprises a heterogeneous collection of malignancies that are typically associated with a poor prognosis and a lack of effective treatment options. We retrospectively evaluated the clinical utility of targeted next-generation sequencing (NGS) among CUP patients to assist with diagnosis and identify opportunities for molecularly guided therapy.

Patients and Methods

Patients with a CUP at Moffitt Cancer Center who underwent NGS between January 1, 2014 and December 31, 2019, were eligible for study inclusion. Next-generation sequencing results were assessed to determine the frequency of clinically actionable molecular alterations, and chart reviews were performed to ascertain the number of patients receiving molecularly guided therapy.

Results

Ninety-five CUP patients were identified for analysis. Next-generation sequencing testing identified options for molecularly guided therapy for 55% (n = 52) of patients. Among patients with molecularly guided therapy options, 33% (n = 17) were prescribed a molecularly guided therapy. The median overall survival for those receiving molecularly guided therapy was 23.6 months. Among the evaluable patients, the median duration of treatment for CUP patients (n = 7) receiving molecular-guided therapy as a first-line therapy was 39 weeks. The median duration of treatment for CUP patients (n = 8) treated with molecularly guided therapy in the second- or later-line setting was 13 weeks. Next-generation sequencing results were found to be suggestive of a likely primary tumor type for 15% (n = 14) of patients.

Conclusion

Next-generation sequencing results enabled the identification of treatment options in a majority of patients and assisted with the identification of a likely primary tumor type in a clinically meaningful subset of patients.

Impact of the timing of tumor assessments on median progression-free survival in clinical trials in advanced cancer patients

Background

Survival-based surrogate endpoints such as progression-free survival (PFS) are commonly used in oncology clinical trials. The evaluation-time bias in the assessment of median disease progression in randomized trials has been suggested by several simulation studies, but never demonstrated in the clinic. We aimed to demonstrate the existence of potential evaluation-time bias by assessing the impact of the timing of tumor assessments on median PFS from control arms without any active treatment of randomized controlled trials involving advanced cancer patients.

Materials and methods

A systematic literature search of English language publications from 1 January 2000 to 7 January 2021 was performed using MEDLINE (PubMed). Eligible trials for our meta-analysis included all randomized clinical trials evaluating anticancer drugs in adult patients with advanced cancers with a control arm without any anticancer drug consisting of best supportive care with or without a placebo. We performed a meta-regression analysis to analyze the correlation between the timing of the first tumor assessment and median PFS in patients randomized in the control arms without any active treatment.

Results

Of 3551 studies screened, 97 eligible trials were retrieved involving 36  747 patients, including 14  229 patients randomized into the control arms. A later first tumor assessment correlated with a prolonged median PFS (R² = 0.44, P < 10⁻⁵).

Conclusions

Our results confirm the existence of potential evaluation-time bias in clinical research that had been suggested by simulation studies. The timing of tumor assessments should be kept the same in precision medicine trials using the PFS ratio as an efficacy endpoint.

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Different timings for the evaluation of efficacy can induce an evaluation-time bias as suggested by simulation studies.

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This bias can be an important issue in precision medicine trials that use each patient as their own controls.

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In our meta-analysis, we found a correlation between the timing of the first tumor assessment and median PFS.

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Our study confirms and quantifies the evaluation-time bias suggested by simulation studies.

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Our study supports having timings of tumor assessments be kept the same in precision medicine trials.

Therapy-related transcriptional subtypes in matched primary and recurrent head and neck cancer

PURPOSE

The genetic relatedness between primary and recurrent head and neck squamous cell carcinomas (HNSCC) reflects the extent of heterogeneity and therapy-driven selection of tumor subpopulations. Yet, current treatment of recurrent HNSCC ignores the molecular characteristics of therapy-resistant tumor populations.

EXPERIMENTAL DESIGN

From 150 tumors, 74 primary HNSCCs were RNA-sequenced and 38 matched primary/recurrent tumor pairs were both, whole-exome and RNA-sequenced. Transcriptome analysis determined the predominant classical (CL), basal (BA) and inflamed-mesenchymal (IMS) transcriptional subtypes according to an established classification. Genomic alterations and clonal compositions of tumors were evaluated from whole-exome data.

RESULTS

While CL and IMS subtypes were more common in primary HNSCC with low recurrence rates, the BA subtype was more prevalent and stable in recurrent tumors. The BA subtype was associated with a transcriptional signature of partial epithelial-to-mesenchymal transition (p-emt) and early recurrence. In 44% of matched cases, the dominant subtype changed from primary to recurrent tumors, preferably from IMS to BA or CL. Gene set enrichment analysis identified upregulation of Hypoxia, p-emt and radiation resistance signatures and downregulation of tumor inflammation in recurrences compared to index tumors. A relevant subset of primary/recurrent tumor pairs presented no evidence for a common clonal origin.

CONCLUSIONS

Our study showed a high degree of genetic and transcriptional heterogeneity between primary/recurrent tumors, suggesting therapy-related selection of a transcriptional subtype with characteristics unfavorable for therapy. We conclude that therapy decisions should be based on genetic and transcriptional characteristics of recurrences rather than primary tumors to enable optimally tailored treatment strategies.

Cost-effectiveness of precision cancer medicine-current challenges in the use of next generation sequencing for comprehensive tumour genomic profiling and the role of clinical utility frameworks (Review)

Precision cancer medicine (PCM) is an emerging paradigm in oncology, which includes tumour comprehensive genomic profiling (CGP) to enable molecularly guided therapy. However, cost-effectiveness analyses of PCM are faced with several challenges and, thus, its cost-effectiveness remains unclear. Early trials using only molecularly guided therapy were faced with the challenge of providing adequate measures of outcome, which probably explains the modest treatment benefits demonstrated. Endpoints like the progression-free survival (PFS)2/PFS1 ratio may assist in overcoming this issue. Moreover, specific tumour subtypes appear to benefit more from PCM. Costs associated with next-generation sequencing (NGS) for CGP are decreasing, but targeted therapy itself represents a major cost driver. CGP not only enables prediction of response to treatment, but also resistance, and could thus prevent administration of unnecessary (and costly) therapies. In clinical practice, the presence of clinical frameworks, such as the Recommendations for the Use of NGS for Patients with Metastatic Cancers from the ESMO Precision Medicine Working Group, and the ESMO Scale for Clinical Actionability of Molecular Targets, are essential in appropriately identifying situations where PCM is clinically meaningful, thereby improving its cost-effectiveness.

Small cell lung cancer; recent advances of its biology and therapeutic perspective

Lung cancer is historically divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). While the therapeutic efficacy of NSCLC has improved due to the development of molecular targeted therapy and immune checkpoint inhibitors (ICIs) treatment, there has been very slow progress in the therapeutic advances of SCLC. Since SCLC is a deadly disease with rapid progression and early metastasis and comprises approximately 10% of lung cancer cases, more attention should be given to the therapeutic strategy for SCLC. Most SCLC cases respond to cytotoxic drugs, cisplatin, and etoposide. The objective response rate to the standard regimen is reported to be approximately 70% that is sufficient as standard therapy. However, almost all tumors recur and become refractory to chemotherapy which is the most important problem of this deadly disease. Recently, for the first time in several decades, ICIs have changed the standard therapy for SCLC. It must be emphasized that although ICIs paved the new way for SCLC therapy, more precise and effective therapy for SCLC is desired. Unfortunately, precise molecular mechanisms of SCLC are yet to be understood. Recent elaborate studies on the cell biology of SCLC uncovered several important aspects of molecular mechanisms. Gene profiling of cancer cells can be done using modern technology like next-generation sequencing (NGS). In this minireview, we describe the advances of modern technology in SCLC research and consider future therapeutic strategies based on the molecular mechanisms of SCLC.

Therapeutic implications of cancer gene amplifications without mRNA overexpression: silence may not be golden

Amplifications of oncogenic genes are often considered actionable. However, not all patients respond. Questions have therefore arisen regarding the degree to which amplifications, especially non-focal ones, mediate overexpression. We found that a subset of high-level gene amplifications (≥ 6 copies) (from The Cancer Genome Atlas database) was not over-expressed at the RNA level. Unexpectedly, focal amplifications were more frequently silenced than non-focal amplifications. Most non-focal amplifications were not silenced; therefore, non-focal amplifications, if over-expressed, may be therapeutically tractable. Furthermore, specific silencing of high-level focal or non-focal gene amplifications may explain resistance to drugs that target the relevant gene product.

Rationale and design of the CRAFT (Continuous ReAssessment with Flexible ExTension in Rare Malignancies) multicenter phase II trial

BACKGROUND

Approvals of cancer therapeutics are primarily disease entity specific. Current molecular diagnostic approaches frequently identify actionable alterations in rare cancers or rare subtypes of common cancers for which the corresponding treatments are not approved and unavailable within clinical trials due to entity-related eligibility criteria. Access may be negotiated with health insurances. However, approval rates vary, and critical information required for a scientific evaluation of treatment-associated risks and benefits is not systematically collected. Thus clinical trials with optimized patient selection and comprehensive molecular characterization are essential for translating experimental treatments into standard care.

PATIENTS AND METHODS

Continuous ReAssessment with Flexible ExTension in Rare Malignancies (CRAFT) is an open-label phase II trial for adults with pretreated, locally advanced, or metastatic solid tumors. Based on the evaluation by a molecular tumor board, patients are assigned to combinations of six molecularly targeted agents and a programmed death-ligand 1 (PD-L1) antagonist within seven study arms focusing on (i) BRAF V600 mutations; (ii) ERBB2 amplification and/or overexpression, activating ERBB2 mutations; (iii) ALK rearrangements, activating ALK mutations; (iv and v) activating PIK3CA and AKT mutations, other aberrations predicting increased PI3K-AKT pathway activity; (vi) aberrations predicting increased RAF-MEK-ERK pathway activity; (vii) high tumor mutational burden and other alterations predicting sensitivity to PD-L1 inhibition. The primary endpoint is the disease control rate (DCR) at week 16; secondary and exploratory endpoints include the progression-free survival ratio, overall survival, and patient-reported outcomes. Using Simon's optimal two-stage design, 14 patients are accrued for each study arm. If three or fewer patients achieve disease control, the study arm is stopped. Otherwise, 11 additional patients are accrued. If the DCR exceeds 7 of 25 patients, the null hypothesis is rejected for the respective study arm.

CONCLUSIONS

CRAFT was activated in October 2021 and will recruit at 10 centers in Germany.

TRIAL REGISTRATION NUMBERS

EudraCT: 2019-003192-18; ClinicalTrials.gov: NCT04551521.

Early-stage economic analysis of research-based comprehensive genomic sequencing for advanced cancer care

Genomic research is driving discovery for future population benefit. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia’s Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm–matched usual care controls. We undertake a cost consequence analysis and estimate 1-year effects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival differences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary benefit for assumed willingness to pay thresholds. We identified 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system effect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival differences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTA-informed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival benefit at a cost that is within healthcare systems’ willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research.

Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA

The primary objective of the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) project is to develop standard analysis protocols and quality control metrics for use in DNA testing to enhance scientific research and precision medicine. This study reports a targeted next-generation sequencing (NGS) method that will enable more accurate detection of actionable mutations in circulating tumor DNA (ctDNA) clinical specimens. To accomplish this, a synthetic internal standard spike-in was designed for each actionable mutation target, suitable for use in NGS following hybrid capture enrichment and unique molecular index (UMI) or non-UMI library preparation. When mixed with contrived ctDNA reference samples, internal standards enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position in each sample. True-positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity.

A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.

Therapeutic Actionability of Circulating Cell-Free DNA Alterations in Carcinoma of Unknown Primary

Cancer of unknown primary (CUP) is a metastatic disease with unidentifiable primary tumor. Somatic alterations can be assessed noninvasively via liquid biopsies interrogating cell-free DNA (cfDNA).

METHODS

We evaluated 1,931 patients with CUP with a cfDNA next-generation sequencing panel (73-74 genes).

RESULTS

Overall, 1,739 patients (90%) had ≥ 1 cfDNA alteration. We then explored alteration actionability (per the levels of evidence from the OncoKB database); 825 patients (47.4% of 1,739) had level 1, level 2, or resistance/R1 alterations. Among 40 clinically annotated patients with CUP who had cfDNA evaluated, higher degrees of matching treatment to alterations (Matching Score > 50% v ≤ 50%) was the only variable predicting improved outcome: longer median progression-free survival (10.4 v 2.5 months; P = .002), overall survival (13.4 v 5.7 months; P = .07, trend), and higher clinical benefit rate (stable disease ≥ 6 months/partial response/complete response; 83% v 25%; P = .003).

CONCLUSION

In summary, cfDNA frequently reveals strong level-of-evidence actionable alterations in CUP, and high degrees of matching to therapy correlates with better outcomes.

Knowledge bases and software support for variant interpretation in precision oncology

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

EGFR Pathway-Based Gene Signatures of Druggable Gene Mutations in Melanoma, Breast, Lung, and Thyroid Cancers

EGFR, BRAF, PIK3CA, and KRAS genes play major roles in EGFR pathway, and accommodate activating mutations that predict response to many targeted therapeutics. However, connections between these mutations and EGFR pathway expression patterns remain unexplored. Here, we investigated transcriptomic associations with these activating mutations in three ways. First, we compared expressions of these genes in the mutant and wild type tumors, respectively, using RNA sequencing profiles from The Cancer Genome Atlas project database (n = 3660). Second, mutations were associated with the activation level of EGFR pathway. Third, they were associated with the gene signatures of differentially expressed genes from these pathways between the mutant and wild type tumors. We found that the upregulated EGFR pathway was linked with mutations in the BRAF (thyroid cancer, melanoma) and PIK3CA (breast cancer) genes. Gene signatures were associated with BRAF (thyroid cancer, melanoma), EGFR (squamous cell lung cancer), KRAS (colorectal cancer), and PIK3CA (breast cancer) mutations. However, only for the BRAF gene signature in the thyroid cancer we observed strong biomarker diagnostic capacity with AUC > 0.7 (0.809). Next, we validated this signature on the independent literature-based dataset (n = 127, fresh-frozen tissue samples, AUC 0.912), and on the experimental dataset (n = 42, formalin fixed, paraffin embedded tissue samples, AUC 0.822). Our results suggest that the RNA sequencing profiles can be used for robust identification of the replacement of Valine at position 600 with Glutamic acid in the BRAF gene in the papillary subtype of thyroid cancer, and evidence that the specific gene expression levels could provide information about the driver carcinogenic mutations.

Targeting CDC7 potentiates ATR-CHK1 signaling inhibition through induction of DNA replication stress in liver cancer

BACKGROUND

Liver cancer is one of the most commonly diagnosed cancers and the fourth leading cause of cancer-related death worldwide. Broad-spectrum kinase inhibitors like sorafenib and lenvatinib provide only modest survival benefit to patients with hepatocellular carcinoma (HCC). This study aims to identify novel therapeutic strategies for HCC patients.

METHODS

Integrated bioinformatics analyses and a non-biased CRISPR loss of function genetic screen were performed to identify potential therapeutic targets for HCC cells. Whole-transcriptome sequencing (RNA-Seq) and time-lapse live imaging were performed to explore the mechanisms of the synergy between CDC7 inhibition and ATR or CHK1 inhibitors in HCC cells. Multiple in vitro and in vivo assays were used to validate the synergistic effects.

RESULTS

Through integrated bioinformatics analyses using the Cancer Dependency Map and the TCGA database, we identified ATR-CHK1 signaling as a therapeutic target for liver cancer. Pharmacological inhibition of ATR or CHK1 leads to robust proliferation inhibition in liver cancer cells having a high basal level of replication stress. For liver cancer cells that are resistant to ATR or CHK1 inhibition, treatment with CDC7 inhibitors induces strong DNA replication stress and consequently such drugs show striking synergy with ATR or CHK1 inhibitors. The synergy between ATR-CHK1 inhibition and CDC7 inhibition probably derives from abnormalities in mitosis inducing mitotic catastrophe.

CONCLUSIONS

Our data highlights the potential of targeting ATR-CHK1 signaling, either alone or in combination with CDC7 inhibition, for the treatment of liver cancer.

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.

Deciphering the cancer microenvironment from bulk data with EcoTyper

In this issue of Cell, Luca, Steen et al. develop the EcoTyper software to deconvolve tumor-microenvironment interactions from high volume bulk transcriptomics data. They demonstrate its effectiveness in improving predictions for tumor progression and patient prognosis for a variety of tumor types from multiple data sources.

Cancer Therapy Guided by Mutation Tests: Current Status and Perspectives

The administration of many cancer drugs is tailored to genetic tests. Some genomic events, e.g., alterations of EGFR or BRAF oncogenes, result in the conformational change of the corresponding proteins and call for the use of mutation-specific compounds. Other genetic perturbations, e.g., HER2 amplifications, ALK translocations or MET exon 14 skipping mutations, cause overproduction of the entire protein or its kinase domain. There are multilocus assays that provide integrative characteristics of the tumor genome, such as the analysis of tumor mutation burden or deficiency of DNA repair. Treatment planning for non-small cell lung cancer requires testing for EGFR, ALK, ROS1, BRAF, MET, RET and KRAS gene alterations. Colorectal cancer patients need to undergo KRAS, NRAS, BRAF, HER2 and microsatellite instability analysis. The genomic examination of breast cancer includes testing for HER2 amplification and PIK3CA activation. Melanomas are currently subjected to BRAF and, in some instances, KIT genetic analysis. Predictive DNA assays have also been developed for thyroid cancers, cholangiocarcinomas and urinary bladder tumors. There is an increasing utilization of agnostic testing which involves the analysis of all potentially actionable genes across all tumor types. The invention of genomically tailored treatment has resulted in a spectacular improvement in disease outcomes for a significant portion of cancer patients.

A phase II study of monalizumab in patients with recurrent/metastatic squamous cell carcinoma of the head and neck: The I1 cohort of the EORTC-HNCG-1559 UPSTREAM trial

PURPOSE

Monalizumab is a monoclonal antibody targeting the inhibitory natural killer group 2A (NKG2A) receptor localised on natural killer (NK) and T cells. Its ligand, the human leukocyte antigen E (HLA-E), is overexpressed in squamous cell carcinoma of the head and neck (SCCHN). By targeting the HLA-E-NKG2A pathway, monalizumab may enhance NK and T cell activity.

EXPERIMENTAL DESIGN

The UPSTREAM trial is a biomarker-driven umbrella trial studying targeted therapies and immunotherapies in patients with recurrent/metastatic (R/M) SCCHN progressing after platinum therapy. The immunotherapy 1 (I1) cohort was a phase II, single-arm substudy evaluating monalizumab (10 mg/kg intravenously on day 1 of a 14-day cycle). The primary end-point was the objective response (OR) rate (Response Evaluation Criteria in Solid Tumours 1.1) over the first 16 weeks. A two-stage Simon design was used (H1 15%, H0 3%, α 8%, power 90%) with pre-planned interruption of accrual if no OR was observed after the first 25 patients.

RESULTS

Twenty-six eligible patients were enrolled. Seventeen (65%) patients had received ≥2 previous lines of systemic treatment, and 15 (58%) patients were PD(-L)1 inhibitor pretreated. No OR was observed. Stable disease was observed in 6 patients (23%) with a median duration of 3.8 months (95% confidence interval [CI]: 2.7-NE). The median progression-free survival and overall survival were 1.7 months (95% CI: 1.5-1.8) and 6.7 months (95% CI: 3.0-9.6), respectively. The most frequent treatment-related adverse event was grade I/II fatigue (19%).

CONCLUSIONS

Monalizumab monotherapy has limited activity in R/M SCCHN. The I1 cohort did not meet its primary objective. Monalizumab combined with durvalumab is under investigation within UPSTREAM.

Targeting ARID1A mutations in cancer

Genes encoding SWI/SNF chromatin remodeling complex subunits are collectively mutated in approximately 20% of human cancers. ARID1A is a SWI/SNF subunit gene whose protein product binds DNA. ARID1A gene alterations result in loss of function. It is the most commonly mutated member of the SWI/SNF complex, being aberrant in ∼6% of cancers overall, including ovarian clear cell cancers (∼45% of patients) and uterine endometrioid cancers (∼37%). ARID1A has a crucial role in regulating gene expression that drives oncogenesis or tumor suppression. In particular, ARID1A participates in control of the PI3K/AKT/mTOR pathway, immune responsiveness to cancer, EZH2 methyltransferase activity, steroid receptor modulation, DNA damage checkpoints, and regulation of p53 targets and KRAS signaling. A variety of compounds may be of benefit in ARID1A-altered cancers: immune checkpoint blockade, and inhibitors of mTOR, EZH2, histone deacetylases, ATR and/or PARP. ARID1A alterations may also mediate resistance to platinum chemotherapy and estrogen receptor degraders/modulators.

Crosstalk Between the Tumor Microenvironment and Cancer Cells: A Promising Predictive Biomarker for Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have changed the landscape of cancer treatment and are emerging as promising curative treatments in different type of cancers. However, only a small proportion of patients have benefited from ICIs and there is an urgent need to find robust biomarkers for individualized immunotherapy and to explore the causes of immunotherapy resistance. In this article, we review the roles of immune cells in the tumor microenvironment (TME) and discuss the effects of ICIs on these cell populations. We discuss the potential of the functional interaction between the TME and cancer cells as a predictive biomarker for ICIs. Furthermore, we outline the potential personalized strategies to improve the effectiveness of ICIs with precision.

Ex Vivo Drug Screening Informed Targeted Therapy for Metastatic Parotid Squamous Cell Carcinoma

The purpose of ex vivo drug screening in the context of precision oncology is to serve as a functional diagnostic method for therapy efficacy modeling directly on patient-derived tumor cells. Here, we report a case study using integrated multiomics ex vivo drug screening approach to assess therapy efficacy in a rare metastatic squamous cell carcinoma of the parotid gland. Tumor cells isolated from lymph node metastasis and distal subcutaneous metastasis were used for imaging-based single-cell resolution drug screening and reverse-phase protein array-based drug screening assays to inform the treatment strategy after standard therapeutic options had been exhausted. The drug targets discovered on the basis of the ex vivo measured drug efficacy were validated with histopathology, genomic profiling, and in vitro cell biology methods, and targeted treatments with durable clinical responses were achieved. These results demonstrate the use of serial ex vivo drug screening to inform adjuvant therapy options prior to and during treatment and highlight HER2 as a potential therapy target also in metastatic squamous cell carcinoma of the salivary glands.

Molecular profiling of advanced malignancies guides first-line N-of-1 treatments in the I-PREDICT treatment-naïve study

Background

Malignancies are molecularly complex and become more resistant with each line of therapy. We hypothesized that offering matched, individualized combination therapies to patients with treatment-naïve, advanced cancers would be feasible and efficacious. Patients with newly diagnosed unresectable/metastatic, poor-prognosis cancers were enrolled in a cross-institutional prospective study.

Methods

A total of 145 patients were included in the study. Genomic profiling (tissue and/or circulating tumor DNA) was performed in all patients, and PD-L1 immunohistochemistry, tumor mutational burden, and microsatellite status assessment were performed in a subset of patients. We evaluated safety and outcomes: disease-control rate (stable disease for ≥ 6 months or partial or complete response), progression-free survival (PFS), and overall survival (OS).

Results

Seventy-six of 145 patients (52%) were treated, most commonly for non-colorectal gastrointestinal cancers, carcinomas of unknown primary, and hepatobiliary malignancies (53% women; median age, 63 years). The median number of deleterious genomic alterations per patient was 5 (range, 0–15). Fifty-four treated patients (71%) received ≥ 1 molecularly matched therapy, demonstrating the feasibility of administering molecularly matched therapy. The Matching Score, which reflects the percentage of targeted alterations, correlated linearly with progression-free survival (R² = 0.92; P = 0.01), and high (≥ 60%) Matching Score was an independent predictor of improved disease control rate [OR 3.31 (95% CI 1.01–10.83), P = 0.048], PFS [HR 0.55 (0.28–1.07), P = 0.08], and OS [HR 0.42 (0.21–0.85), P = 0.02]. Serious adverse event rates were similar in the unmatched and matched groups.

Conclusions

Personalized combination therapies targeting a majority of a patient’s molecular alterations have antitumor activity as first-line treatment. These findings underscore the feasibility and importance of using tailored N-of-1 combination therapies early in the course of lethal malignancies.

Trial registration

I-PREDICT (NCT02534675) was registered on August 25, 2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00969-w.

Does large NGS panel analysed using exome tumour sequencing improve the management of advanced non-small-cell lung cancers?

INTRODUCTION

Non-small-cell lung cancer (NSCLC) is one of the most common and deadly cancers. Several molecular drivers of oncogene addiction are now known to be strong predictive biomarkers for target therapies. Advances in large Next Generation Sequencing (LNGS) have improved the ability to detect potentially targetable mutations. However, the integration of LNGS into clinical management in an individualized manner remains challenging.

METHODS

In this single-center observational study we included all patients with advanced NSCLC who underwent LNGS. Somatic and germline exome analysis was performed with a restriction on 323 cancer related genes. Variants were classified and Molecular Tumour Board (MTB) made therapeutic propositions.

RESULTS

We performed LNGS analysis in 281 patients with advanced NSCLC between March 2015 and January 2018. Technical failure occurred in only 3% of cases. Three hundred and fifty-six targetable mutations were detected. At least one targetable mutation was found in 209 patients. For all these patients, the MTB was able to recommend treatment with a targeted agent based on the evaluation of the tumour's genetic profile and treatment history. Twenty-nine patients (13.9%) were subsequently treated with an MTB-recommended targeted therapy. We did not observe any improvement in terms of clinical benefit for these patients.

CONCLUSIONS

In this case series, we show that including LNGS into routine clinical management was feasible but does not appear to provide clinical benefit in the management of patients with advanced NSCLC.

Targeted Therapy of Papillary Thyroid Cancer: A Comprehensive Genomic Analysis

Background

A limited number of targeted therapy options exist for papillary thyroid cancer (PTC) to date. Based on genetic alterations reported by the "The Cancer Genome Atlas (TCGA)", we explored whether PTC shows alterations that may be targetable by drugs approved by the FDA for other solid cancers.

Methods

Databases of the National Cancer Institute and MyCancerGenome were screened to identify FDA-approved drugs for targeted therapy. Target genes were identified using Drugbank. Genetic alterations were classified into conferring drug sensitivity or resistance using MyCancerGenome, CiViC, TARGET, and OncoKB. Genomic data for PTC were extracted from TCGA and mined for alterations predicting drug response.

Results

A total of 129 FDA-approved drugs with 128 targetable genes were identified. One hundred ninety-six (70%) of 282 classic, 21 (25%) of 84 follicular, and all 30 tall-cell variant PTCs harbored druggable alterations: 259 occurred in 29, 39 in 19, and 31 in 2 targetable genes, respectively. The BRAF V600 mutation was seen in 68% of classic, 16% of follicular variant, and 93% of tall-cell variant PTCs. The RET gene fusion was seen in 8% of classic PTCs, NTRK1 and 3 gene fusions in 3%, and other alterations in <2% of classic variant PTCs. Ninety-nine of 128 (77%) FDA-approved targetable genes did not show any genetic alteration in PTC. Beside selective and non-selective BRAF-inhibitors, no other FDA-approved drug showed any frequent predicted drug sensitivity (<10%).

Conclusion

Treatment strategies need to focus on resistance mechanisms to BRAF inhibition and on genetic alteration-independent alternatives rather than on current targeted drugs.

Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

SCRUM-Japan GI-SCREEN and MONSTAR-SCREEN: Path to the realization of biomarker-guided precision oncology in advanced solid tumors

Comprehensive genomic profiling enables the detection of genomic biomarkers in advanced solid tumors. However, efficient patient screening for the success of precision oncology remains challenging due to substantial barriers, such as genotyping costs and accessibility to matched therapies. To address these challenges, we launched GI‐SCREEN, a nationwide gastrointestinal cancer genomic screening project within the SCRUM‐Japan network in 2015 with the specific purpose of matching patients with a diverse portfolio of affiliated interventional targeted therapy trials. Subsequently, we initiated the molecular profiling projects GOZILA, MONSTAR‐SCREEN‐1, and MONSTAR‐SCREEN‐2, which incorporate tissue and plasma multiomics approaches to accurately identify patients with advanced solid tumors who would benefit from matched therapies. These projects have led to a significant increase in patient participation in targeted clinical trials and the approval of several therapeutics and companion diagnostics. Additionally, clinicogenomic analyses utilizing the SCRUM‐Japan database have provided new insights into the molecular mechanisms of advanced solid tumors. In this review, we describe the path to the realization of cancer precision medicine for patients with advanced solid tumors based on the SCRUM‐Japan GI‐SCREEN and MONSTAR‐SCREEN platforms.

Molecular profiling of advanced solid tumours. The impact of experimental molecular-matched therapies on cancer patient outcomes in early-phase trials: the MAST study

INTRODUCTION

Molecular-matched therapies have revolutionized cancer treatment. We evaluated the improvement in clinical outcomes of applying an in-house customized Next Generation Sequencing panel in a single institution.

METHODS

Patients with advanced solid tumors were molecularly selected to receive a molecular-matched treatment into early phase clinical trials versus best investigators choice, according to the evaluation of a multidisciplinary molecular tumor board. The primary endpoint was progression-free survival (PFS) assessed by the ratio of patients presenting 1.3-fold longer PFS on matched therapy (PFS2) than with prior therapy (PFS1).

RESULTS

Of a total of 231 molecularly screened patients, 87 were eligible for analysis. Patients who received matched therapy had a higher median PFS2 (6.47 months; 95% CI, 2.24-14.43) compared to those who received standard therapy (2.76 months; 95% CI, 2.14-3.91, Log-rank p = 0.022). The proportion of patients with a PFS2/PFS1 ratio over 1.3 was significantly higher in the experimental arm (0.33 vs 0.08; p = 0.008).

DISCUSSION

We demonstrate the pivotal role of the institutional molecular tumor board in evaluating the results of a customized NGS panel. This process optimizes the selection of available therapies, improving disease control. Prospective randomized trials are needed to confirm this approach and open the door to expanded drug access.

Targeting extracellular and juxtamembrane FGFR2 mutations in chemotherapy-refractory cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) has emerged as a promising candidate for precision medicine, especially in the case of activating FGFR2 gene fusions. In addition to fusions, a considerable fraction of iCCA patients reveals FGFR2 mutations, which might lead to uncontrolled activation of the FGFR2 pathway but are mostly of unknown functional significance. A current challenge for molecular tumor boards (MTB) is to predict the functional consequences of such FGFR2 alterations to guide potential treatment decisions. We report two iCCA patients with extracellular and juxtamembrane FGFR2 mutations. After in silico investigation of the alterations and identification of activated FGFR2 downstream targets in tumor specimens by immunohistochemistry and transcriptome analysis, the MTB recommended treatment with an FGFR-inhibiting tyrosine kinase inhibitor. Both patients developed a rapidly detectable and prolonged partial response to treatment. These two cases suggest an approach to characterize further detected FGFR2 mutations in iCCA to enable patients´ selection for a successful application of the FGFR -inhibiting drugs.

A phase i study of ixazomib and erlotinib in patients with advanced solid tumors

BACKGROUND

Preclinical studies have shown that the combined inhibition of EGFR and NF-kB pathways to target the RalB/TBK1 pathway led to synergistic antitumor activity. Based on this rationale, we conducted a Phase I dose-escalation study combining the EGFR inhibitor erlotinib with the NF-kB inhibitor ixazomib in advanced solid tumors. Patients and methods. Patients with advanced solid tumors were eligible. The bayesian optimal interval phase I dose escalation design was used to establish the maximum tolerated dose and recommended phase 2 dose (RP2D). Results. Nineteen patients with a range of solid tumors were enrolled. The most common treatment-related adverse events of any grade were diarrhea (42.1%, 8/19), followed by rash (36.8%, 7/19) and nausea (21.1%, 4/19). The combination RP2D for oral ixazomib was 4.0 mg on days 1, 8, and 15 of a 28-day cycle, with oral erlotinib 150 mg daily. While no patient achieved RECIST v1.1 objective responses, 3 patients with advanced sarcoma experienced durable RECIST v1.1 stable disease ≥ 6 months (8.4, 10.6, and 15.7 months) and the best response was -13% decrease in clear cell sarcoma. Conclusions. The combination of erlotinib and ixazomib was safe and well tolerated among patients with advanced cancer, with preliminary signals of antitumor activity in patients with advanced sarcoma.

Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species

Understanding relationships between spontaneous cancer in companion (pet) canines and humans can facilitate biomarker and drug development in both species. Towards this end we developed an experimental-bioinformatic protocol that analyzes canine transcriptomics data in the context of existing human data to evaluate comparative relevance of canine to human cancer. We used this protocol to characterize five canine cancers: melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, in 60 dogs. We applied an unsupervised, iterative clustering method that yielded five co-expression modules and found that each cancer exhibited a unique module expression profile. We constructed cancer models based on the co-expression modules and used the models to successfully classify the canine data. These canine-derived models also successfully classified human tumors representing the same cancers, indicating shared cancer biology between canines and humans. Annotation of the module genes identified cancer specific pathways relevant to cells-of-origin and tumor biology. For example, annotations associated with melanin production (PMEL, GPNMB, and BACE2), synthesis of bone material (COL5A2, COL6A3, and COL12A1), synthesis of pulmonary surfactant (CTSH, LPCAT1, and NAPSA), ribosomal proteins (RPL8, RPS7, and RPLP0), and epigenetic regulation (EDEM1, PTK2B, and JAK1) were unique to melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, respectively. In total, 152 biomarker candidates were selected from highly expressing modules for each cancer type. Many of these biomarker candidates are under-explored as drug discovery targets and warrant further study. The demonstrated transferability of classification models from canines to humans enforces the idea that tumor biology, biomarker targets, and associated therapeutics, discovered in canines, may translate to human medicine.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

The Landscape of Pediatric Precision Oncology: Program Design, Actionable Alterations, and Clinical Trial Development

Over the last years, various precision medicine programs have been developed for pediatric patients with high-risk, relapsed, or refractory malignancies, selecting patients for targeted treatment through comprehensive molecular profiling. In this review, we describe characteristics of these initiatives, demonstrating the feasibility and potential of molecular-driven precision medicine. Actionable events are identified in a significant subset of patients, although comparing results is complicated due to the lack of a standardized definition of actionable alterations and the different molecular profiling strategies used. The first biomarker-driven trials for childhood cancer have been initiated, but until now the effect of precision medicine on clinical outcome has only been reported for a small number of patients, demonstrating clinical benefit in some. Future perspectives include the incorporation of novel approaches such as liquid biopsies and immune monitoring as well as innovative collaborative trial design including combination strategies, and the development of agents specifically targeting aberrations in childhood malignancies.

Technological Advances in Tumor-On-Chip Technology: From Bench to Bedside

Simple Summary

Various 3D in vitro tumor models are rapidly advancing cancer research. Unlike animal models, they can be produced quickly and are amenable to high-throughput studies. Growing tumor spheroids in microfluidic tumor-on-chip platforms has particularly elevated the capabilities of such models. Tumor-on-chip devices can mimic multiple aspects of the dynamic in vivo tumor microenvironment in a precisely controlled manner. Moreover, new technologies for the on- and off-chip analysis of these tumor mimics are continuously emerging. There is thus an urgent need to review the latest developments in this rapidly progressing field. Here, we present an overview of the technological advances in tumor-on-chip technology by reviewing state-of-the-art tools for on-chip analysis. In particular, we evaluate the potential for tumor-on-chip technology to guide personalized cancer therapies. We strive to appeal to cancer researchers and biomedical engineers alike, informing on current progress, while provoking thought on the outstanding developments needed to achieve clinical-stage research.

Abstract

Tumor-on-chip technology has cemented its importance as an in vitro tumor model for cancer research. Its ability to recapitulate different elements of the in vivo tumor microenvironment makes it promising for translational medicine, with potential application in enabling personalized anti-cancer therapies. Here, we provide an overview of the current technological advances for tumor-on-chip generation. To further elevate the functionalities of the technology, these approaches need to be coupled with effective analysis tools. This aspect of tumor-on-chip technology is often neglected in the current literature. We address this shortcoming by reviewing state-of-the-art on-chip analysis tools for microfluidic tumor models. Lastly, we focus on the current progress in tumor-on-chip devices using patient-derived samples and evaluate their potential for clinical research and personalized medicine applications.

Molecularly profiled trials: toward a framework of actions for the "nil actionables"

The sequencing of tumour or blood samples is increasingly used to stratify patients into clinical trials of molecularly targeted agents, and this approach has frequently demonstrated clinical benefit for those who are deemed eligible. But what of those who have no clear and evident molecular driver? What of those deemed to have "nil actionable" mutations? How might we deliver better therapeutic opportunities for those left behind in the clamour toward stratified therapeutics? And what significant learnings lie hidden in the data we amass but do not interrogate and understand? This Perspective article suggests a holistic approach to the future treatment of such patients, and sets a framework through which significant additional patient benefit might be achieved. In order to deliver upon this framework, it encourages and invites the clinical community to engage more enthusiastically and share learnings with colleagues in the early drug discovery community, in order to deliver a step change in patient care.

Assigning evidence to actionability: An introduction to variant interpretation in precision cancer medicine

Modern concepts in precision cancer medicine are based on increasingly complex genomic analyses and require standardized criteria for the functional evaluation and reporting of detected genomic alterations in order to assess their clinical relevance. In this article, we propose and address the necessary steps in systematic variant evaluation consisting of bioinformatic analysis, functional annotation and clinical interpretation, focusing on the latter two aspects. We discuss the role and clinical application of current variant classification systems and point out their scope and limitations. Finally, we highlight the significance of the molecular tumor board as a platform for clinical decision-making based on genomic analyses.

Feasibility of next-generation sequencing in clinical practice: results of a pilot study in the Department of Precision Medicine at the University of Campania 'Luigi Vanvitelli'

Background

The emerging role of next-generation sequencing (NGS) targeted panels is revolutionising our approach to cancer patients, providing information on gene alterations helpful for diagnosis and clinical decision, in a short time and with acceptable costs.

Materials and methods

In this work, we evaluated the clinical application of FoundationOne CDx test, a hybrid capture-based NGS. This test identifies alterations in 324 genes, tumour mutational burden and genomic signatures as microsatellite instability. The decision to obtain the NGS assay for a particular patient was done according to investigator’s choice.

Results

Overall, 122 tumour specimens were analysed, of which 84 (68.85%) succeeded. The success rate was influenced by type of specimen formalin-fixed paraffin embedded (FFPE block vs FFPE slides), by origin of the sample (surgery vs biopsy) and by time of fixation (<5 years vs ≥5 years). The most frequent subgroups of effective reports derived from colorectal cancer (25 samples), non-small-cell lung cancer (16 samples), ovarian cancer (10 samples), biliary tract cancer (9 samples), breast cancer (7 samples), gastric cancer (7 samples). The most frequent alterations found in whole population referred to TP53 (45.9%), KRAS (19.6%) and APC (13.9%). Furthermore, we performed an analysis of patients in whom this comprehensive genomic profiling (CGP) had a relevance for the patient’s disease.

Conclusions

On our opinion, CGP could be proposed in clinical practice in order to select patients that could most benefit from the analysis proposed, like patients with good performance status without any available treatments or with unexpected resistance to a therapy.

Volumetric growth tracking of patient-derived cancer organoids using optical coherence tomography

Patient-derived cancer organoids (PCOs) are in vitro organotypic models that reflect in vivo drug response, thus PCOs are an accessible model for cancer drug screening in a clinically relevant timeframe. However, current methods to assess the response of PCOs are limited. Here, a custom swept-source optical coherence tomography (OCT) system was used to rapidly evaluate volumetric growth and drug response in PCOs. This system was optimized for an inverted imaging geometry to enable high-throughput imaging of PCOs. An automated image analysis framework was developed to perform 3D single-organoid tracking of PCOs across multiple time points over 48 hours. Metabolic inhibitors and cancer therapies decreased PCOs volumetric growth rate compared to control PCOs. Single-organoid tracking improved sensitivity to drug treatment compared to a pooled analysis of changes in organoid volume. OCT provided a more accurate assessment of organoid volume compared to a volume estimation method based on 2D projections. Single-organoid tracking with OCT also identified heterogeneity in drug response between solid and hollow PCOs. This work demonstrates that OCT and 3D single-organoid tracking are attractive tools to monitor volumetric growth and drug response in PCOs, providing rapid, non-destructive methods to quantify heterogeneity in PCOs.