A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary.

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

MDM4 is a rational target for treating breast cancers with mutant p53.

Mutation of the key tumour suppressor p53 defines a transition in the progression towards aggressive and metastatic breast cancer (BC) with the poorest outcome. Specifically, the p53 mutation frequency exceeds 50% in triple-negative BC. Key regulators of mutant p53 that facilitate its oncogenic functions are potential therapeutic targets. We report here that the MDM4 protein is frequently abundant in the context of mutant p53 in basal-like BC samples. Importantly, we show that MDM4 plays a critical role in the proliferation of these BC cells. We demonstrate that conditional knockdown (KD) of MDM4 provokes growth inhibition across a range of BC subtypes with mutant p53, including luminal, Her2+ and triple-negative BCs. In vivo, MDM4 was shown to be crucial for the establishment and progression of tumours. This growth inhibition was mediated, at least in part, by the cell cycle inhibitor p27. Depletion of p27 together with MDM4 KD led to recovery of the proliferative capacity of cells that were growth-inhibited by MDM4 KD alone. Consistently, we identified low levels of p27 expression in basal-like tumours corresponding to high levels of MDM4 and p53. This predicts a signature for a subset of tumours that may be amenable to therapies targeted towards MDM4 and mutant p53. The therapeutic potential of MDM4 as a target in BC with mutant p53 was shown in vitro by use of a small-molecule inhibitor. Overall, our study supports MDM4 as a novel therapeutic target for BC expressing mutant p53. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Immune and genomic biomarkers of immunotherapy response in cancer of unknown primary.

BACKGROUND: Cancer of unknown primary (CUP) is a heterogeneous group of metastatic cancers where a primary tissue of origin (TOO) is uncertain. Most patients with CUP have limited treatment options and poor survival outcomes. Immune checkpoint inhibitors (ICIs) can be efficacious in some patients with CUP, but the optimal predictive biomarkers are unknown. We therefore assessed immune and genomic biomarkers as well as predicted TOO in patients with CUP, including a subset treated with ICIs. METHODS: Patients with CUP were subject to gene-expression profiling (GEP) and DNA panel sequencing. Immune and stromal-related gene expression was explored by NanoString, including genes associated with immunotherapy response (IR) in other solid malignancies. ICI responsive cancer types were assigned based on Food and Drug Administration-approved indications, and either detection of a latent primary tumor or the TOO was suspected based on genomics informed pathology review. Tumor mutation burden (TMB) and gene mutations were also assessed. RESULTS: A total of 219 patients with CUP were included, 215 assessed for TOO in a previous study, with the majority (163) receiving both RNA and DNA tests. Of GEP profiled cases, 33% (59/175) had a high IR gene-expression score. Of the DNA sequenced cases, 16% (32/203) had high TMB (>10 mutations/Mb), including two with mismatch repair deficiency. Low correlation was observed between TMB and an IR score (R=0.26, p<0.001). Among 110 CUPs with a latent primary or suspected TOO, 47% (52/110) belonged to ICI-responsive cancer types. More than half of the CUPs had at least one feature that may predict ICI response (high IR score, high TMB, ICI-responsive cancer type). Among patients with CUP treated with ICIs, 8/28 (29%) responded (2 complete responses and 6 partial responses). Among non-responders, 9 had stable and 11 had progressive disease. All responders had a high IR score (7/8) and/or high TMB (3/8), while most (5/8) belonged to ICI-responsive cancer types. These features were detected at a lower frequency in non-responders and mostly in patients with stable disease. CONCLUSIONS: A significant fraction of CUP tumors had genomic features previously associated with ICI response. High IR score was the most sensitive predictive feature of ICI response, warranting evaluation in a larger patient series.

γδ T Cells in Merkel Cell Carcinomas Have a Proinflammatory Profile Prognostic of Patient Survival.

Merkel cell carcinomas (MCC) are immunogenic skin cancers associated with viral infection or UV mutagenesis. To study T-cell infiltrates in MCC, we analyzed 58 MCC lesions from 39 patients using multiplex-IHC/immunofluorescence (m-IHC/IF). CD4+ or CD8+ T cells comprised the majority of infiltrating T lymphocytes in most tumors. However, almost half of the tumors harbored prominent CD4/CD8 double-negative (DN) T-cell infiltrates (>20% DN T cells), and in 12% of cases, DN T cells represented the majority of T cells. Flow cytometric analysis of single-cell suspensions from fresh tumors identified DN T cells as predominantly Vδ2- γδ T cells. In the context of γδ T-cell inflammation, these cells expressed PD-1 and LAG3, which is consistent with a suppressed or exhausted phenotype, and CD103, which indicates tissue residency. Furthermore, single-cell RNA sequencing (scRNA-seq) identified a transcriptional profile of γδ T cells suggestive of proinflammatory potential. T-cell receptor (TCR) analysis confirmed clonal expansion of Vδ1 and Vδ3 clonotypes, and functional studies using cloned γδ TCRs demonstrated restriction of these for CD1c and MR1 antigen-presenting molecules. On the basis of a 13-gene γδ T-cell signature derived from scRNA-seq analysis, gene-set enrichment on bulk RNA-seq data showed a positive correlation between enrichment scores and DN T-cell infiltrates. An improved disease-specific survival was evident for patients with high enrichment scores, and complete responses to anti-PD-1/PD-L1 treatment were observed in three of four cases with high enrichment scores. Thus, γδ T-cell infiltration may serve as a prognostic biomarker and should be explored for therapeutic interventions.See related Spotlight on p. 600.

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients. METHODS: We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively. INTERPRETATION: The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform. FUNDING: NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia.