Precision Medicine in Hematology 2021: Definitions, Tools, Perspectives, and Open Questions

Surveying Hematologists' Perceptions and Readiness to Embrace Artificial Intelligence in Diagnosis and Treatment Decision-Making

AIM

This study aims to explore the critical dimension of assessing the perceptions and readiness of hematologists to embrace artificial intelligence (AI) technologies in their diagnostic and treatment decision-making processes.

METHODS

This study used a cross-sectional design for collecting data related to the perceptions and readiness of hematologists using a validated online questionnaire-based survey. Both hematologists (MD) and postgraduate MD students in hematology were included in the study. A total of 188 participants, including 35 hematologists (MD) and 153 MD hematology students, completed the survey.

RESULTS

Major challenges include "AI's level of autonomy" and "the complexity in the field of medicine." Major barriers and risks identified include "lack of trust," "management's level of understanding," "dehumanization of healthcare," and "reduction in physicians' skills." Statistically significant differences in perceptions of benefits including resources (p=0.0326, p<0.05) and knowledge (p=0.0262, p<0.05) were observed between genders. Older physicians were observed to be more concerned about the use of AI compared to younger physicians (p<0.05).

CONCLUSION

While AI use in hematology diagnosis and treatment decision-making is positively perceived, issues such as lack of trust, transparency, regulations, and poor AI awareness can affect the adoption of AI.

Vienna Cancer Stem Cell Club (VCSCC): 20 year jubilee and future perspectives

INTRODUCTION

The Vienna Cancer Stem Cell Club (VCSCC) was launched by a group of scientists in Vienna in 2002.

AREAS COVERED

Major aims of the VCSCC are to support research on cancer stem cells (CSC) in hematopoietic malignancies and to translate CSC-related markers and targets into clinical application. A primary focus of research in the VCSCC is the leukemic stem cell (LSC). Between 2013 and 2021, members of the VCSCC established a special research program on myeloproliferative neoplasms and since 2008, members of the VCSCC run the Ludwig Boltzmann Institute for Hematology and Oncology. In all these years, the VCSCC provided a robust intellectual platform for translational hematology and LSC research in Vienna. Furthermore, the VCSCC interacts with several national and international study groups and societies in the field. Representatives of the VCSCC also organized a number of international meetings and conferences on neoplastic stem cells, including LSC, in the past 15 years, and contributed to the definition and classification of CSC/LSC and related pre-malignant and malignant conditions.

EXPERT OPINION

The VCSCC will continue to advance the field and to develop LSC-detecting and LSC-eradicating concepts through which diagnosis, prognostication, and therapy of blood cancer patients should improve.

Precision Medicine in Therapy of Non-solid Cancer

The development and approval of the tyrosine kinase inhibitor imatinib in 2001 has heralded the advance of directed therapy options. Today, an armamentarium of targeted therapeutics is available and enables the use of precision medicine in non-solid cancer. Precision medicine is guided by the detection of tumor-specific and targetable characteristics. These include pathogenic fusions and/or mutations, dependency on specific signaling pathways, and the expression of certain cell surface markers. Within the first part, we review approved targeted therapies for the compound classes of small molecule inhibitors, antibody-based therapies and cellular therapies. Particular consideration is given to the underlying pathobiology and the respective mechanism of action. The second part emphasizes on how biomarkers, whether they are of diagnostic, prognostic, or predictive relevance, are indispensable tools to guide therapy choice and management in precision medicine. Finally, the examples of acute myeloid leukemia, chronic lymphocytic leukemia, and chronic myeloid leukemia illustrate how integration of these biomarkers helps to tailor therapy.

Microphysiological Drug-Testing Platform for Identifying Responses to Prodrug Treatment in Primary Leukemia

Despite increasing survival rates of pediatric leukemia patients over the past decades, the outcome of some leukemia subtypes has remained dismal. Drug sensitivity and resistance testing on patient-derived leukemia samples provide important information to tailor treatments for high-risk patients. However, currently used well-based drug screening platforms have limitations in predicting the effects of prodrugs, a class of therapeutics that require metabolic activation to become effective. To address this issue, a microphysiological drug-testing platform is developed that enables co-culturing of patient-derived leukemia cells, human bone marrow mesenchymal stromal cells, and human liver microtissues within the same microfluidic platform. This platform also enables to control the physical interaction between the diverse cell types. Herein, it is made possible to recapitulate hepatic prodrug activation of ifosfamide in their platform, which is very difficult in traditional well-based assays. By testing the susceptibility of primary patient-derived leukemia samples to the prodrug ifosfamide, sample-specific sensitivities to ifosfamide in primary leukemia samples are identified. The microfluidic platform is found to enable the recapitulation of physiologically relevant conditions and the testing of prodrugs including short-lived and unstable metabolites. The platform holds great potential for clinical translation and precision chemotherapy selection.

Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine

Drug testing in patient biopsy-derived cells can identify potent treatments for patients suffering from relapsed or refractory hematologic cancers. Here we investigate the use of weakly supervised deep learning on cell morphologies (DML) to complement diagnostic marker-based identification of malignant and nonmalignant cells in drug testing. Across 390 biopsies from 289 patients with diverse blood cancers, DML-based drug responses show improved reproducibility and clustering of drugs with the same mode of action. DML does so by adapting to batch effects and by autonomously recognizing disease-associated cell morphologies. In a post hoc analysis of 66 patients, DML-recommended treatments led to improved progression-free survival compared with marker-based recommendations and physician's choice-based treatments. Treatments recommended by both immunofluorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identification of effective personalized treatments.

SIGNIFICANCE

We have recently demonstrated that image-based drug screening in patient samples identifies effective treatment options for patients with advanced blood cancers. Here we show that using deep learning to identify malignant and nonmalignant cells by morphology improves such screens. The presented workflow is robust, automatable, and compatible with clinical routine. This article is highlighted in the In This Issue feature, p. 476.

Ex Vivo Chemosensitivity Profiling of Acute Myeloid Leukemia and Its Correlation With Clinical Response and Outcome to Chemotherapy

We evaluated the predictive value of the ex-vivo PharmaFlow PM platform in measuring the pharmacological activity of drug combinations consisting of 20 different chemotherapy regimens (20 Tx) administered in 104 acute myeloid leukemia (AML) patients. The predicted sensitivities of alternative treatments for each patient were ranked in five 20% categories, from resistant to sensitive (Groups 1–5). The complete remission (CR) rates of the five groups were 0%, 12.5%, 38.5%, 50.0%, and 81.3%, respectively. The heat map showed a good relationship between drug sensitivity with CR (Group 4 + 5 vs. Group 1 + 2+3: 77.5% vs. 27.3%, p = 0.002) and the European Leukemia Net risk group (22.6% vs. 63.6%, p = 0.015). The predicted coincidence rate was 90.9% in Group 1 + 2 and 81.3% in Group 5. According to the recommendations of the PharmaFlow PM platform, the CR rate would have increased by about 16.3% in one cycle. The overall survival (OS) was shorter in patients predicted to be resistant (Group 1 + 2 vs. Group 3 + 4+5, p = 0.086). In multivariable analysis, CR after one cycle was an independent prognostic factor for OS [p = 0.001; 95% CI 0.202 (0.080–0.511)], and ex-vivo chemosensitivity was a potential predictive factor for OS [p = 0.078; 95% CI 0.696 (0.465–1.041)]. To conclude, the PharmaFlow PM platform is a rapid and valuable tool for predicting clinical response and outcomes in AML patients.

A Hopeful Leap Forward by Multicentric Cooperation for Precision-Based Therapy for Very Resistant, Relapsed, or Refractory Childhood Leukemia

Pikman and colleagues report the results of a multicentric prospective clinical trial of the leukemia precision-based therapy (LEAP) consortium that combines identification of targetable lesions in drug-resistant childhood leukemia, tiered based on evidence for genomic lesions and drug target, validation of matching small-molecule targeted agents, and treatment of individual patients. The study demonstrates the impact of genomic information on disease classification, treatment guidance, and translational research, but also illustrates the challenges for target prediction and trial design for increasingly heterogeneous and smaller subgroups of patients.See related article by Pikman et al., p. 1424.