Dynamic assessment of measurable residual disease in favorable-risk acute myeloid leukemia in first remission, treatment, and outcomes

We aimed to investigate outcomes of different post-remission treatment (PRT) choices based on dynamic measurable residual disease (MRD) by multiparameter flow cytometry in favorable-risk AML (FR-AML). Four hundred and three younger patients with FR-AML in first complete remission (CR1) were enrolled in this registry-based cohort study, including 173 who received chemotherapy (CMT), 92 autologous stem cell transplantation (auto-SCT), and 138 allogeneic SCT (allo-SCT). The primary endpoint was the 5-year overall survival (OS). Subgroup analyses were performed based on dynamic MRD after the 1st, 2nd, and 3rd courses of chemotherapy. In subgroups of patients with negative MRD after 1 or 2 course of chemotherapy, comparable OS was observed among the CMT, auto-SCT, and allo-SCT groups (p = 0.340; p = 0.627, respectively). But CMT and auto-SCT had better graft-versus-host-disease-free, relapse-free survival (GRFS) than allo-SCT in both subgroups. For patients with negative MRD after three courses of chemotherapy, allo-SCT had better disease-free-survival than CMT (p = 0.009). However, OS was comparable among the three groups (p = 0.656). For patients with persistently positive MRD after 3 courses of chemotherapy or recurrent MRD, allo-SCT had better OS than CMT and auto-SCT (p = 0.011; p = 0.029, respectively). Dynamic MRD might improve therapy stratification and optimize PRT selection for FR-AML in CR1.

Infantile mediastinal neuroblastoma presenting as an oncologic emergency: usefulness of serum-based MYCN gene amplification analysis for risk stratification

We reported two infantile cases of mediastinal neuroblastoma with life-threatening tracheal obstructions presenting as oncologic emergencies that were successfully treated per tentative risk classification using serum-based MYCN gene amplification (MNA) analysis. Tentative risk stratification based on age, tumour location and serum-based MNA status may be useful in patients with neuroblastoma presenting as oncologic emergencies who require urgent therapy stratification but for whom tumor-based molecular diagnoses cannot be established.

Dynamic Assessment of Measurable Residual Disease in Favorable-Risk Acute Myeloid Leukemia in First Remission, Treatment, and Outcomes

Purpose:We aimed to investigate outcomes of different post-remission treatment (PRT) choices based on dynamic measurable residual disease (MRD) in favorable-risk AML (FR-AML). Methods: Four hundred and three younger patients with FR-AML in first complete remission (CR1) were enrolled in this registry-based cohort study, including 173 who received chemotherapy (CMT), 92 autologous stem cell transplantation (auto-SCT), and 138 allogeneic SCT (allo-SCT). Subgroup analyses were performed based on dynamic MRD after the first, second, and third courses of chemotherapy. The primary endpoint was the 5-year overall survival (OS). Results: For patients with negative MRD after 1 course of chemotherapy, comparable OS was observed among the CMT, auto-SCT and allo-SCT groups (p=.284). But CMT had better graft-versus-host-disease-free, relapse-free survival (GRFS) than allo-SCT (p=.027). For patients with negative MRD after 2 courses of chemotherapy, comparable OS was also observed among the three groups (p=.967). However, CMT and auto-SCT had better GRFS than allo-SCT (p=.045; p=.020, respectively). For patients with negative MRD after 3 courses of chemotherapy, allo-SCT had better disease-free-survival than CMT (p=.011). However, OS was comparable among the three groups (p=.177). For patients with persistently positive MRD after 3 courses of chemotherapy or recurrent MRD, allo-SCT had better OS than CMT and auto-SCT (p=.012; p=.046, respectively). Conclusions: Dynamic MRD might improve therapy stratification and optimize PRT selection for FR-AML in CR1. CMT might be preferable for patients with negative MRD within 3 cycles of chemotherapy while allo-SCT for patients with persistently positive MRD after 3 cycles of chemotherapy and recurrent MRD. Disclosures No relevant conflicts of interest to declare.

Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification

Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified neuroblastomas. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown. Here, we investigate whether transcriptional subtyping of MYCN non-amplified neuroblastomas can identify molecular subtypes with discrete prognosis and therapeutic vulnerabilities. Using tumour expression data and ConsensusClusterPlus, we demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup 2 has the worst prognosis and this group shows a "MYCN" signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup 3 is characterised by an "inflamed" gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates unique prognosis and vulnerability to investigational therapies. We propose that matching baseline tumour subtype to therapy may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.

Automated immunostaining-free prediction of breast carcinoma ER and PR status, Ki67 count, patient therapy stratification index and quantification of prognostic quiescence burden in TNBC from pretreatment H&E stained histopathology images in breast cancer.

e12535 Background: Categorical combinations of ER, PR, HER2, and Ki67 levels are traditionally used to classify patients into luminal A and B-like subtypes in order to inform treatment choice. Accounting for nearly 70% of all breast malignancies, luminal cancer is heterogeneous, harboring subtypes with distinct molecular profiles and clinical outcomes. Although most patients with luminal-type disease respond well to endocrine therapy alone, some develop recurrences benefiting from additional cytotoxic therapy. Identifying such cases a priori remains a challenge but would enable patients to be spared the debilitating side-effects of ineffective chemotherapy. In this regard, the efficacy of chemotherapy and disease recurrence relate to (i) ER driven G1/S perturbations and/or (ii) quiescent cell populations arrested in the G0/G1 phase of cell cycle. This study aimed to develop a histopathology whole slide image (WSI)-based, low cost, rapid and automated approach to: (i) predict ER/PR/Ki67 status, (ii) quantify quiescence burden, (iii) develop a G1/S-based patient stratification system for luminal A/B patients, and (iv) achieve a quiescence burden-based stratification of TNBC patients. Methods: This investigation centered on the initial clinical validation of a novel, immunostaining-free technology which uses information extracted from pre-treatment hematoxylin and eosin (H&E) stained slide WSIs alone to achieve these aims. Unlike conventional artificial intelligence-based approaches, the underlying proprietary algorithm and its prediction criteria are based on deterministic, hard-coded observational relationships of continuous scales drawn from WSI morphological features. In this instance, these represent tumor-related biological pathway disruptions and mitotic checkpoint perturbations, where G1/S perturbations enable luminal subtype stratification, and G0/G1 perturbations reflect quiescence burden. Back projecting the algorithm’s quiescence burden output on to the original WSIs enables morphological patterns to be mapped to quiescence burden.

Clinical Perspective and Translational Oncology of Liquid Biopsy

The term liquid biopsy (LB) refers to the study of circulating tumor cells, circulating tumors nucleic acids free of cells or contained in exosomes, and information about platelets associated with tumors. LB can be performed in different biofluids and allows the limitations of tissue biopsy to be overcome offering possibilities of tumor identification reflecting in real time tumor heterogeneity. In addition, LB allows screening and early detection of cancer, real-time monitoring of therapy, stratification and therapeutic intervention, a therapeutic target and resistance mechanism, and a risk of metastatic relapse. Currently, LB has been shown to be effective for its application in different types of tumors including lung, colorectal, prostate, melanoma, breast and pancreatic cancer, by the determination and identification of biomarkers that with a high probability have the potential to change the way in which medical oncology could predict the course of the disease. These biomarkers make it possible to capture the heterogeneity of the cancer, monitor its clonal evolution, indicate new treatments or retreatments and evaluate the responses to different evolutionary and/or therapeutic pressures in the cancer disease.

Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.