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The continuous improvement in cancer care over the past decade has led to a gradual decrease in cancer-related deaths. This is largely attributed to improved treatment and disease management strategies. Early detection of recurrence using blood-based biomarkers such as circulating tumour DNA (ctDNA) is being increasingly used in clinical practice. Emerging real-world data shows the utility of ctDNA in detecting molecular residual disease and in treatment-response monitoring, helping clinicians to optimize treatment and surveillance strategies. Many studies have indicated ctDNA to be a sensitive and specific biomarker for recurrence. However, most of these studies are largely observational or anecdotal in nature, and peer-reviewed data regarding the use of ctDNA are mainly indication-specific. Here we provide general recommendations on the clinical utility of ctDNA and how to interpret ctDNA analysis in different treatment settings, especially in patients with solid tumours. Specifically, we provide an understanding around the implications, strengths and limitations of this novel biomarker and how to best apply the results in clinical practice. This Perspective reviews the utility and interpretation of circulating tumour DNA for the detection of residual and recurrent cancers and provides recommendations regarding its clinical application for a variety of solid tumours.

Patients with locally advanced resectable (LAR) gastric/gastroesophageal junction (G/GEJ) adenocarcinomas have a high recurrence risk despite pre- and post-operative treatment. In the PLAGAST prospective study (NCT02674373), we investigated the ability of circulating tumor DNA (ctDNA) to predict treatment response and improve risk stratification. Plasma samples were prospectively collected before neoadjuvant therapy (NAT), during-NAT, post-NAT, and post-surgery. The primary endpoint was recurrence-free survival (RFS), and the secondary endpoints were overall survival (OS), tumor regression grade (TRG), and pathological tumor stage. ctDNA positivity decreased over these four therapeutic timelines (69.6%, 51.2%, 26.8%, and 20%, respectively). ctDNA-positivity was associated with significantly worse outcomes during-NAT (RFS: HR = 6.17, P  = 0.002; OS: HR = 4.71, P  = 0.022), post-NAT (RFS: HR = 5.26, P  = 0.001; OS: HR = 7.35, P  = 0.001) and after surgery (RFS: HR = 12.94, P  < 0.0001; OS: HR = 14.54, P  < 0.0001). Patients with early ctDNA clearance during NAT had better outcomes compared to those who cleared ctDNA post-NAT, while patients who remained ctDNA-positive pre-, during-, and post-NAT had worse outcomes (RFS: HR = 18.57, P  = 0.01; OS: HR = 16.06, P  = 0.007). Our data suggests that longitudinal ctDNA monitoring is prognostic of patient outcomes and may guide therapeutic decision-making in patients with LAR G/GEJ adenocarcinoma. Despite pre- and post-operative treatment, patients with locally advanced resectable gastric/gastroesophageal junction adenocarcinomas have a high recurrence risk. Here, the authors analyse a prospective cohort of patients and find ctDNA status is correlated with survival outcomes.

This manuscript summarizes current thinking on the value and promise of evolving circulating tumor cell (CTC) technologies for cancer patient diagnosis, prognosis, and response to therapy, as well as accelerating oncologic drug development. Moving forward requires the application of the classic steps in biomarker development–analytical and clinical validation and clinical qualification for specific contexts of use. To that end, this review describes methods for interactive comparisons of proprietary new technologies, clinical trial designs, a clinical validation qualification strategy, and an approach for effectively carrying out this work through a public-private partnership that includes test developers, drug developers, clinical trialists, the US Food & Drug Administration (FDA) and the US National Cancer Institute (NCI).

The utility of spatial immunobiomarker quantitation in prognostication and therapeutic prediction is actively being investigated in triple-negative breast cancer (TNBC). Here, with high-plex quantitative digital spatial profiling, we map and quantitate intraepithelial and adjacent stromal tumor immune protein microenvironments in systemic treatment-naïve (female only) TNBC to assess the spatial context in immunobiomarker-based prediction of outcome. Immune protein profiles of CD45-rich and CD68-rich stromal microenvironments differ significantly. While they typically mirror adjacent, intraepithelial microenvironments, this is not uniformly true. In two TNBC cohorts, intraepithelial CD40 or HLA-DR enrichment associates with better outcomes, independently of stromal immune protein profiles or stromal TILs and other established prognostic variables. In contrast, intraepithelial or stromal microenvironment enrichment with IDO1 associates with improved survival irrespective of its spatial location. Antigen-presenting and T-cell activation states are inferred from eigenprotein scores. Such scores within the intraepithelial compartment interact with PD-L1 and IDO1 in ways that suggest prognostic and/or therapeutic potential. This characterization of the intrinsic spatial immunobiology of treatment-naïve TNBC highlights the importance of spatial microenvironments for biomarker quantitation to resolve intrinsic prognostic and predictive immune features and ultimately inform therapeutic strategies for clinically actionable immune biomarkers. The tumor immune microenvironment is an important determinant of clinical outcomes and therapeutic responses in patients with triple-negative breast cancer (TNBC). Here the authors perform digital spatial profiling of tumor tissues to characterize the spatial immunobiology of treatment-naïve TNBC.

BackgroundGiven its high recurrence risk, guidelines recommend systemic therapy for most patients with early-stage triple-negative breast cancer (TNBC). While some clinicopathologic factors and tumor-infiltrating lymphocytes (TILs) are known to be prognostic in patients receiving chemotherapy, their prognostic implications in systemically untreated patients remain unknown.MethodsFrom a cohort of 9982 women with surgically treated non-metastatic breast cancer, all patients with clinically reported ER-negative/borderline (≤10%) disease were selected for central assessment of ER/PR/HER2, histopathology, Ki-67, and TILs. The impact of these parameters on invasive disease-free survival (IDFS) and overall survival (OS) was assessed using Cox proportional hazards models.ResultsSix hundred five patients met the criteria for TNBC (ER/PR < 1% and HER2 negative). Most were T1–2 (95%), N0–1 (86%), grade 3 (88%), and had a Ki-67 >15% (75%). Histologically, 70% were invasive carcinoma of no special type, 16% medullary, 8% metaplastic, and 6% apocrine. The median stromal TIL content was 20%. Four hundred twenty-three (70%) patients received adjuvant chemotherapy. Median OS follow-up was 10.6 years. On multivariate analysis, only higher nodal stage, lower TILs, and the absence of adjuvant chemotherapy were associated with worse IDFS and OS. Among systemically untreated patients (n = 182), the 5-year IDFS was 69.9% (95% CI 60.7–80.5) [T1a: 82.5% (95% CI 62.8–100), T1b: 67.5% (95% CI 51.9–87.8) and T1c: 67.3% (95% CI 54.9–82.6)], compared to 77.8% (95% CI 68.3–83.6) for systemically treated T1N0. Nodal stage and TILs remained strongly associated with outcomes.ConclusionsIn early-stage TNBC, nodal involvement, TILs, and receipt of adjuvant chemotherapy were independently associated with IDFS and OS. In systemically untreated TNBC, TILs remained prognostic and the risk of recurrence or death was substantial, even for T1N0 disease.

Key Points The application of several high-throughput genomic and proteomic technologies to address questions in cancer diagnosis, prognosis and prediction generate high-dimensional data sets. The multimodality of high-dimensional cancer data, for example, as a consequence of the heterogeneous and dynamic nature of cancer tissues, the concurrent expression of multiple biological processes and the diverse and often tissue-specific activities of single genes, can confound both simple mechanistic interpretations of cancer biology and the generation of complete or accurate gene signal transduction pathways or networks. The mathematical and statistical properties of high-dimensional data spaces are often poorly understood or inadequately considered. This can be particularly challenging for the common scenario where the number of data points obtained for each specimen greatly exceed the number of specimens. Data are rarely randomly distributed in high-dimensions and are highly correlated, often with spurious correlations. The distances between a data point and its nearest and farthest neighbours can become equidistant in high dimensions, potentially compromising the accuracy of some distance-based analysis tools. Owing to the 'curse of dimensionality' phenomenon and its negative impact on generalization performance, for example, estimation instability, model overfitting and local convergence, the large estimation error from complex statistical models can easily compromise the prediction advantage provided by their greater representation power. Conversely, simpler statistical models may produce more reproducible predictions but their predictions may not always be adequate. Some machine learning methods address the 'curse of dimensionality' in high-dimensional data analysis through feature selection and dimensionality reduction, leading to better data visualization and improved classification. It is important to ensure that the generalization capability of classifiers derived by supervised learning methods from high-dimensional data before using them for cancer diagnosis, prognosis or prediction. Although this can be assessed initially through cross-validation methods, a more rigorous approach is needed, that is, to validate classifier performance using a blind validation data set(s) that was not used during supervised learning. High-dimensional genomic and proteomic data are now commonplace in cancer research. This Review aims to help biologists understand the properties of high-dimensional data spaces and how these affect our ability to derive meaningful information from the data. High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.

Distant metastases account for ~ 90% of cancer deaths and major responses with systemic therapy alone for metastatic cancers are so rare that the National Cancer Institute launched the Exceptional Responders Initiative. Comprehensive involved site radiotherapy (ISRT) is a promising treatment for oligometastases but the role of circulating tumor DNA to confirm durable molecular response following treatment remains unexplored. Among 597 consecutive patients with distant metastases treated with radiation therapy from 2014 to 2021, 133 (22%) were oligometastatic and 464 (78%) were polymetastatic. The 5-year overall survival was 38% for oligometastases vs. 3% for polymetastases ( p  < 0.001). At a median follow-up of 71 months for treated oligometastases, 37 (28%) exceptional responders (ER) remain alive and recurrence free at ≥ 2 year follow-up. Among ER, 49% underwent stereotactic radiotherapy (median 27 Gy in 3 fractions, EQD2 43 Gy), 65% underwent intensity-modulated radiation therapy (median 53 Gy in 24 fractions, EQD2 54 Gy), and 76% received additional systemic therapy. Although ctDNA testing was not possible in most ER due to patient refusal or tumor specimen quality, all 12 ER tested ctDNA-negative. Long-term complete responses, including molecular complete responses, are achievable with comprehensive ISRT in diverse clinical presentations.

Summary Early cancer detection should lead to an overall stage shift, less-intensive treatments and better patient outcomes. Current recommended screening programmes are limited to a handful of individual cancers. A multi-cancer early detection test that simultaneously detects and localises multiple cancers could reduce the morbidity and mortality associated with cancer.

Background Triple negative breast cancer (TNBC) is an aggressive subtype with poor prognosis. We aimed to determine whether circulating tumor DNA (ctDNA) and circulating tumor cell (CTC) could predict response and long-term outcomes to neoadjuvant chemotherapy (NAC). Methods Patients with TNBC were enrolled between 2017–2021 at The University of Texas MD Anderson Cancer Center (Houston, TX). Serial plasma samples were collected at four timepoints: pre-NAC (baseline), 12-weeks after NAC (mid-NAC), after NAC/prior to surgery (post-NAC), and one-year after surgery. ctDNA was quantified using a tumor-informed ctDNA assay (Signatera TM , Natera, Inc.) and CTC enumeration using CellSearch. Wilcoxon and Fisher’s exact tests were used for comparisons between groups and Kaplan–Meier analysis used for survival outcomes. Results In total, 37 patients were enrolled. The mean age was 50 and majority of patients had invasive ductal carcinoma (34, 91.9%) with clinical T2, (25, 67.6%) node-negative disease (21, 56.8%). Baseline ctDNA was detected in 90% (27/30) of patients, of whom 70.4% (19/27) achieved ctDNA clearance by mid-NAC. ctDNA clearance at mid-NAC was significantly associated with pathologic complete response ( p  = 0.02), whereas CTC clearance was not ( p  = 0.52). There were no differences in overall survival (OS) and recurrence-free survival (RFS) with positive baseline ctDNA and CTC. However, positive ctDNA at mid-NAC was significantly associated with worse OS and RFS ( p  = 0.0002 and p  = 0.0034, respectively). Conclusions Early clearance of ctDNA served as a predictive and prognostic marker in TNBC. Personalized ctDNA monitoring during NAC may help predict response and guide treatment.