Next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) supports blood-based genomic profiling but is not yet routinely implemented in the setting of a phase I trials clinic. TARGET is a ...molecular profiling program with the primary aim to match patients with a broad range of advanced cancers to early phase clinical trials on the basis of analysis of both somatic mutations and copy number alterations (CNA) across a 641 cancer-associated-gene panel in a single ctDNA assay. For the first 100 TARGET patients, ctDNA data showed good concordance with matched tumor and results were turned round within a clinically acceptable timeframe for Molecular Tumor Board (MTB) review. When a 2.5% variant allele frequency (VAF) threshold was applied, actionable mutations were identified in 41 of 100 patients, and 11 of these patients received a matched therapy. These data support the application of ctDNA in this early phase trial setting where broad genomic profiling of contemporaneous tumor material enhances patient stratification to novel therapies and provides a practical template for bringing routinely applied blood-based analyses to the clinic.
There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with ...limited transparency and explainability, which constrain their deployment in biomedical settings.
This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods.
We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models.
The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.
Durvalumab is a programmed death-ligand 1 (PD-L1) inhibitor with clinical activity in advanced urothelial cancer (AUC)
. AUC is characterized by several recurrent targetable genomic alterations
. ...This study ( NCT02546661 , BISCAY) combined durvalumab with relevant targeted therapies in biomarker-selected chemotherapy-refractory AUC populations including: (1) fibroblast growth factor receptor (FGFR) inhibitors in tumors with FGFR DNA alterations (FGFRm); (2) pharmacological inhibitor of the enzyme poly-ADP ribose polymerase (PARP) in tumors with and without DNA homologous recombination repair deficiency (HRRm); and (3) TORC1/2 inhibitors in tumors with DNA alteration to the mTOR/PI3K pathway
.This trial adopted a new, biomarker-driven, multiarm adaptive design. Safety, efficacy and relevant biomarkers were evaluated. Overall, 391 patients were screened of whom 135 were allocated to one of six study arms. Response rates (RRs) ranged 9-36% across the study arms, which did not meet efficacy criteria for further development. Overall survival (OS) and progression-free survival (PFS) were similar in the combination arms and durvalumab monotherapy arm. Biomarker analysis showed a correlation between circulating plasma-based DNA (ctDNA) and tissue for FGFRm. Sequential circulating tumor DNA analysis showed that changes to FGFRm correlated with clinical outcome. Our data support the clinical activity of FGFR inhibition and durvalumab monotherapy but do not show increased activity for any of the combinations. These findings question the targeted/immune therapy approach in AUC.
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study revealed a more nuanced role for ML explanation ...models, when these are pragmatically embedded in the clinical context. Despite the general positive attitude of healthcare professionals (HCPs) towards explanations as a safety and trust mechanism, for a significant set of participants there were negative effects associated with confirmation bias, accentuating model over-reliance and increased effort to interact with the model. Also, contradicting one of its main intended functions, standard explanatory models showed limited ability to support a critical understanding of the limitations of the model. However, we found new significant positive effects which repositions the role of explanations within a clinical context: these include reduction of automation bias, addressing ambiguous clinical cases (cases where HCPs were not certain about their decision) and support of less experienced HCPs in the acquisition of new domain knowledge.
Abstract
Specialized transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential ...to encode large-scale biological knowledge. We investigate the encoding and representation of biological knowledge in these models, and its potential utility to support inference in cancer precision medicine—namely, the interpretation of the clinical significance of genomic alterations. We compare the performance of different transformer baselines; we use probing to determine the consistency of encodings for distinct entities; and we use clustering methods to compare and contrast the internal properties of the embeddings for genes, variants, drugs, and diseases. We show that these models do indeed encode biological knowledge, although some of this is lost in fine-tuning for specific tasks. Finally, we analyze how the models behave with regard to biases and imbalances in the dataset.
Molecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision-making within precision medicine. Though in use globally, reporting on these meetings often focuses on the ...small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment.
A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 51 papers were reviewed spanning a 6-year period from 11 different countries.
In total, 20% of patients received treatment through the MTB process. Of those that did not receive treatment, the main reasons were no mutations identified (27%), no actionable mutations (22%) and clinical deterioration (15%). However, data were often incomplete due to inconsistent reporting of MTBs with only 55% reporting on patients having no mutations, 55% reporting on the presence of actionable mutations with no treatment options and 59% reporting on clinical deterioration.
As patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown otherwise promising results in ...cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta-review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. To this end we also address a widespread challenge of the applicability of machine learning in general: reduced training data availability. We do so by augmenting available (but often insufficient) patient cytokine concentrations with statistical knowledge extracted from domain literature. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against the said CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. We evaluate our proposed methods under several design choices, achieving performance of more than 90% in terms of CRS identification accuracy, and showing that many of our choices outperform a purely data-driven alternative. During evaluation with real-world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm.
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•A metareview-based diagnosis process that maximizes the use of external information.•A data augmentation method that addresses the data scarcity problem in healthcare.•An ML-based diagnosis process that offer predictions with a diversity of viewpoints.•An abduction process that backtracks predictions to supporting/refuting evidence.
Summary
Background
AZD4547 is a potent, oral, highly selective fibroblast growth factor receptor (FGFR) inhibitor in clinical development for treating tumours with a range of FGFR aberrations, ...including FGFR mutations, amplifications and fusions.
Methods
This open-label, Phase I, multicentre study (NCT01213160) evaluated the safety, pharmacokinetics, and preliminary antitumour efficacy (RECIST v1.1) of AZD4547 monotherapy in Japanese patients with advanced solid tumours. Part A was a dose-escalation part; Part B was a dose-expansion part in patients with FGFR-amplified tumours, confirmed by fluorescence in situ hybridization.
Results
Thirty patients enrolled in Part A (dose range: 40 mg twice daily bid to 120 mg bid; 160 mg once daily qd), four in Part B (80 mg bid). No dose-limiting toxicities were observed and maximum tolerated dose was not determined. Most common adverse events (AEs; any grade) were: dysgeusia (50% of patients); stomatitis (41%); diarrhoea (38%); hyperphosphataemia (38%); dry mouth (35%). Common grade ≥3 AEs were nausea (12% of patients) and neutropenia (9%). No complete or partial responses were observed: 21/30 patients had stable disease ≥4 weeks in Part A, and 1/4 patients had stable disease ≥10 weeks in Part B. Following single and multiple dosing, absorption rate appeared moderate; peak plasma concentrations generally occurred 3–4 h post-dose, then declined biphasically with terminal half-life ~30 h. Steady state was reached by day 8. Compared with single dosing, plasma concentrations were, on average, 2.4- and 3.3- to 5.4-fold higher after qd and bid dosing, respectively.
Conclusions
AZD4547 was well tolerated in Japanese patients, with best response of stable disease ≥4 weeks.
Health care needs to continuously evolve and innovate to maintain the health of populations. Technology has the potential to enable better patient engagement and ownership, as well as optimise ...therapeutic interventions and data-science approaches to facilitate improved health care decisions. Yet, to date, technological innovation has not resulted in the rate of change that could have been predicted from other sectors. This article discusses multiple reasons for this and proposes a newly tested and deployed solution: the technology clinical trial. The technology clinical trial methodology has been developed through working directly with patients, clinical and medical devicetrial experts. This approach enables researchers to use the complex environment of health care as an opportunity to transform the pace of innovation and create new care pathways. Instead of testing a single innovation, researchers can ‘step back’ and systematically review all areas of the patient's journey for potential optimization. Then integrate novel data science, technological advances, process updates, behavioural science, and patient engagement to co-create a streamlined multidisciplinary solution. As a result, this research has the potential for larger advances due to the emergent benefits that can arise when the individual elements work together as a whole. These potential benefits are then robustly tested, characterised and measured in the trial environment to ensure that future application of the innovative pathway is supported by the robust empirical data health care requires.
ObjectivesCOVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. ...However, there is little information regarding longitudinal changes in laboratory prognostic variables in relation to disease progression in hospitalised patients with COVID-19.Design and settingThis retrospective observational report describes disease progression from symptom onset, to admission to hospital, clinical response and discharge/death among patients with COVID-19 at a tertiary centre in South East England.ParticipantsSix hundred and fifty-one patients treated for SARS-CoV-2 between March and September 2020 were included in this analysis. Ethical approval was obtained from the HRA Specific Review Board (REC 20/HRA/2986) for waiver of informed consent.ResultsThe majority of patients presented within 1 week of symptom onset. The lowest risk patients had low mortality (1/45, 2%), and most were discharged within 1 week after admission (30/45, 67%). The highest risk patients, as determined by the 4C mortality score predictor, had high mortality (27/29, 93%), with most dying within 1 week after admission (22/29, 76%). Consistent with previous reports, most patients presented with high levels of C reactive protein (CRP) (67% of patients >50 mg/L), D-dimer (98%>upper limit of normal (ULN)), ferritin (65%>ULN), lactate dehydrogenase (90%>ULN) and low lymphocyte counts (81%<lower limit of normal (LLN)). Increases in platelet counts and decreases in CRP, neutrophil:lymphocyte ratio (p<0.001), lactate dehydrogenase, neutrophil counts, urea and white cell counts (all p<0.01) were each associated with discharge.ConclusionsSerial measurement of routine blood tests may be a useful prognostic tool for monitoring treatment response in hospitalised patients with COVID-19. Changes in other biochemical parameters often included in a ‘COVID-19 bundle’ did not show significant association with outcome, suggesting there may be limited clinical benefit of serial sampling. This may have direct clinical utility in the context of escalating healthcare costs of the pandemic.
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