Leveraging molecular structure and bioactivity with chemical language models for de novo drug design

Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method's scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model's ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.

Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model

Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. Using machine learning (ML) techniques we can detect parameters that help us to improve clinical diagnosis, since they are a system for the detection, prediction and treatment of complex data. ML techniques can be valuable for the study of COVID-19, especially because they can uncover complex patterns in large data sets. This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.

Determination of the direct bandgap value in In4Se3thin films

The value and the nature of the bandgap of In₄Se₃are still not well defined, with a large spread of the experimental data between 0.42 and 1.68 eV and an uncertain nature, predicted to be indirect byab initioband structure calculations. Here we report on the optical transmission and photoluminescence (PL) performed in In₄Se₃thin films grown by coevaporation on (0001)-oriented sapphire wafers. The quality of the polycrystalline layers allows the first detection of the excitonic-like transition in the optical absorption of this compound at low temperature. The PL detected under weak laser excitation shows a bound exciton emission at 0.75 eV. Strong laser irradiation reveals a quadratic dependence of the PL intensity on the optical excitation, which demonstrates a stimulated emission at 0.79 eV in relation with an exciton-exciton scattering process. On the basis of a reasonable estimate of the exciton energy, equal to10-15meV, we evaluate the direct bandgap of In₄Se₃to0.82±0.01eV at low temperature.

Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models

Chemical language models (CLMs) can be employed to design molecules with desired properties. CLMs generate new chemical structures in the form of textual representations, such as the simplified molecular input line entry system (SMILES) strings. However, the quality of these de novo generated molecules is difficult to assess a priori. In this study, we apply the perplexity metric to determine the degree to which the molecules generated by a CLM match the desired design objectives. This model-intrinsic score allows identifying and ranking the most promising molecular designs based on the probabilities learned by the CLM. Using perplexity to compare "greedy" (beam search) with "explorative" (multinomial sampling) methods for SMILES generation, certain advantages of multinomial sampling become apparent. Additionally, perplexity scoring is performed to identify undesired model biases introduced during model training and allows the development of a new ranking system to remove those undesired biases.

Beam Search for Automated Design and Scoring of Novel ROR Ligands with Machine Intelligence*

Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. Herein, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORγ. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

Bidirectional Molecule Generation with Recurrent Neural Networks

Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL.

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.

Kinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.

InSe as a case between 3D and 2D layered crystals for excitons

InSe is a promising material in many aspects where the role of excitons is decisive. Here we report the sequential appearance in its luminescence of the exciton, the biexciton, and the P-band of the exciton-exciton scattering while the excitation power increases. The strict energy and momentum conservation rules of the P-band are used to reexamine the exciton binding energy. The new value ≥20 meV is markedly higher than the currently accepted one (14 meV), being however well consistent with the robustness of the excitons up to room temperature. A peak controlled by the Sommerfeld factor is found near the bandgap (~1.36 eV). Our findings supported by theoretical calculations taking into account the anisotropic material parameters question the pure three-dimensional character of the exciton in InSe, assumed up to now. The refined character and parameters of the exciton are of paramount importance for the successful application of InSe in nanophotonics.

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

Epitaxy of oligothiophenes on alkali metal hydrogen phthalates: Simulations and experiments

Three alkali metal hydrogen phthalate salts (denoted as XAP, X = K⁺, Rb⁺, and Cs⁺) are chosen as substrates for the growth of quaterthiophene (4T) and hexathiophene (6T) thin films by organic molecular beam epitaxy to study the influence of gradual changes of surface unit cell parameters on the epitaxialgrowth. The increment of substrate lattice parameters increases the distance between the planes that define the furrows where oligothiophene molecules lie, while keeping unmodified the interactions between the overlayer and the substrate. Atom-atom potential simulations predict the preferential azimuthal orientations of the overlayer, which are compared with those experimentally observed. The agreement between simulations and experiments about contact planes and orientation of the crystalline domains in the films is satisfactory for both 4T/XAP and 6T/XAP. The increasing width of the surface furrows existing on moving from KAP to RbAP and CsAP does not cause any significant variation of the orientation and density of the overlayer domains, demonstrating that the interaction between overlayer and substrate is the key factor guiding organic epitaxialgrowth.

[Internal medicine in the hospital setting]

Management of all pathologies, and in particular that of the most frequent ones, should whenever possible be based on robust evidence and arguments. New studies published this year enable rationalizing of screening in certain clinical situations, more adequate treatment of others, and open the way for novel and apparently very effective treatments. Whether it be the screening of carotid stenosis, the treatment of pericarditis, of heart failure, of chronic obstructive lung disease or spontaneous bacterial peritonitis, paradigm changes are conceivable. This selective review of the literature summarizes certain studies published this year.

["Mobile" ECMO]

ECMO (extracorporeal membrane oxygenation) is a cardiac or respiratory support which uses the principle of extracorporeal circulation (ECC). It consists of a pump generating an output as well as a membrane oxygenating blood and removing CO2. Thanks to an ECMO mobile team, expert caregivers can now perform the circulatory support in primary centers and then transfer patients under assistance to the referral center. After a brief summary of the two different anatomical approaches (veno-arterial and veno-venous) as well as their indications, the authors will share their experience of two transferred patients under ECMO to Geneva. Referral center and ECMO mobile team concepts will then be detailed focusing on the present situation in Switzerland.

Ultrafast gaseous "half-wave plate"

We demonstrate that filaments generated by ultrashort laser pulses can induce a remarkably large birefringence in Argon over its whole length, resulting in an ultrafast "half-wave plate" for a copropagating probe beam. This birefringence originates from the difference between the nonlinear refractive indices induced by the filament on the axes parallel and orthogonal to its polarization. An angle of 45 degrees between the filament and the probe polarizations allows the realization of ultrafast Kerr-gates, with a switching time ultimately limited by the duration of the filamenting pulse.

Magnetically Driven Growth of Anthracene Thin Films by Organic Molecular Beam Deposition

The possible use of a static magnetic field during organic molecular beam deposition of thin molecular films for inducing some preferential growth is discussed and the magnetic properties of diamagnetic molecules and molecular crystals are recalled. Considering prototypical materials, namely anthracene molecules and potassium phthalate substrates, which interact and may give rise to polycrystalline films with specific orientations, we show that in the presence of a magnetic field the films display a macroscopic preferential orientation as a result of minimization of the magnetic energy contribution. A very good agreement between the results of optical spectroscopy, atomic force microscopy, and predictions made on the basis of the anisotropic magnetic susceptibility of anthracene is found.

Directional dispersion in absorbance spectra of oligothiophene crystals

Due to the large oscillator strength of the first molecular transition in oligothiophenes, a strong directional dispersion of the b(u) exciton transition is expected originating from the macroscopic polarization field. Examining such dispersion unambiguously usually requires different faces to be accessible for the optical measurements. Alternatively, measurements carried out at different angles of incidence are met with intrinsic limits due to the peculiarities of wave propagation in such anisotropic systems. In order to demonstrate these limits along with the experimental difficulties involved, we examine refraction and absorption of light in these crystals and discuss the effects of directional dispersion on the absorbance spectra of quaterthiophene crystals.

Haemocompatibility evaluation of DLC- and SiC-coated surfaces

Diamond-like carbon (DLC) and silicon carbide (SiC) coatings are attractive because of low friction coefficient, high hardness, chemical inertness and smooth finish, which they provide to biomedical devices. Silicon wafers (Si(waf)) and silicone rubber (Si(rub)) plates were coated using plasma-enhanced chemical vapour deposition (PE-CVD) techniques. This article describes: 1- the characterization of modified surfaces using attenuated total reflection-Fourier transform infrared spectroscopy (ATR/FTIR) and contact angle measurements, 2- the results of three in-vitro haemocompatibility assays. Coated surfaces were compared to uncoated materials and various substrates such as polymethylmethacrylate (PMMA), polyethylene (LDPE), polydimethylsiloxane (PDMS) and medical steel (MS). Thrombin generation, blood platelet adhesion and complement convertase activity tests revealed the following classification, from the most to the least heamocompatible surface: Si(rub)/ DLC-Si(rub)/ DLC-Si(waf)/ LDPE/ PDMS/ SiC-Si(waf)/ Si(waf)/ PMMA/ MS. The DLC coating surfaces delayed the clotting time, tended to inhibit the platelet and complement convertase activation, whereas SiC-coated silicon wafer can be considered as thrombogenic. This study has taken into account three events of the blood activation: coagulation, platelet activation and inflammation. The response to those events is an indicator of the in vitro haemocompatibility of the different surfaces and it allows us to select biomaterials for further in vivo blood contacting investigations.

Equation of state data for iron at pressures beyond 10 Mbar

We present equation of state points for iron, in the pressure range 10-45 Mbar, the first obtained with laser-driven shock waves. The experiment has been performed with the high energy laser Phebus, optically smoothed with Kinoform phase plates. Our results double the set of existing experimental data at very high pressures showing good agreement with the predictions of the quotidian equation of state model and with previous results.

[Re]

A chair conformation comparable to that observed for six-membered rings composed of tetrahedral carbon atoms is found for the cluster anion [Re(6)(µ-H)(5)(CO)(24)](-) (see picture; black spheres: Re, white spheres: µ-H; CO ligands omitted for clarity) in spite of the octahedral coordination at the Re centers. This is the first example of a carbonyl cluster exhibiting a cyclohexane-like geometry of the metallic framework.

In vitro metabolism of bladder carcinogenic nitrosamines by rat liver and urothelial cells

In order to establish the importance of the target organ in the activation of bladder carcinogens, we compared rat liver and urothelial cell alpha-hydroxylation activities using as substrates N-nitrosobutyl(4-hydroxybutyl)amine and its metabolite N-nitrosobutyl(3-carboxypropyl)amine, two potent urinary bladder carcinogens in animals. Previous studies have shown that the production of molecular nitrogen can serve as an indicator of nitrosamine alpha-hydroxylation. The use of doubly 15N-labelled nitrosamines and the gas chromatography-mass spectrometric detection of 15N2 formed gives a measurement of the extent of this metabolic step. Various amounts of 15N-labelled substrates were incubated for 60 min at 37 degrees C with rat liver S9 preparations or urothelial cell homogenates in the presence of a NADPH generating system. Both enzyme sources metabolized 15N-labelled N-nitrosobutyl(4-hydroxybutyl)amine and N-nitrosobutyl(3-carboxypropyl)amine through the alpha-hydroxylation pathway. Using hepatic S9 fractions, 15N2 production from 15N-labelled N-nitrosobutyl(4-hydroxybutyl)amine increased from 1.69 +/- 0.02 nmol/h per mg protein (mean +/- S.E.) to 5.78 +/- 0.5 with substrate concentrations ranging between 0.55 and 5.55 mM. 15N2 produced by urothelial cell homogenates was about 40-50% that of the liver S9. 15N-labelled N-nitrosobutyl(3-carboxypropyl)amine was also metabolized through the alpha-hydroxylation pathway both by hepatic S9 and urothelial cell homogenates, though to a lesser extent. 15N2 production was about 10-times less than from 15N-labelled N-nitrosobutyl(4-hydroxybutyl)amine, but again urothelial cell 15N2 production was about 40-50% that of the liver. Treatment with phenobarbital resulted in a 2.7-fold increase in the 15N2 produced from 15N-labelled N-nitrosobutyl(4-hydroxybutyl)amine by hepatic S9. No effect was observed with urothelial cell homogenates. Acetone treatment had no effect on 15N2 production from 15N-labelled N-nitrosobutyl(4-hydroxybutyl)amine by hepatic S9, but raised 15N2 production by urothelial cell homogenates 1.8 times. Although the liver has a greater capacity than the bladder for activating the 15N-labelled nitrosamines studied, the target organ can metabolize bladder carcinogens, thus increasing the possibility of a local toxic effect. Moreover, the distribution of P-450 isozymes might be different in the bladder and this could affect the metabolism of nitrosamines reportedly formed in the human bladder in some pathological conditions.

Renal and urinary lipid changes associated with an acutely induced renal papillary necrosis in rats

A single dose of 2-bromoethanamine hydrobromide (BEA; 100 mg/kg body weight) given ip to male Wistar rats causes an acute renal papillary necrosis in 24 hr. Oil Red O (ORO) lipid staining is normally confined to the polyunsaturated lipid droplets of the medullary interstitial cells, but ORO-positive material was present in the endothelial cells of the vasa recta 7 hr after BEA treatment. At 24 hr (by which time papillary necrosis had developed), there were also markedly increased quantities of lipid in the cells of the collecting ducts and covering epithelia. At 48 hr totally necrosed areas stained heavily with ORO, and lipid deposits were particularly numerous in the hyperplastic urothelia adjacent to the necrosed region. Epithelial and endothelial accumulation of lipid material also extended into areas of the juxtamedulla and cortex, which appeared normal by routine haematoxylin and eosin staining. Lipid staining is more selective and sensitive for identifying papillary necrosis than routine histology, because neither hexachlorobutadiene-, aminoglycoside-, cis-platin- nor polybrene-induced lesions produce similar histochemical changes. This suggests that the capillary and epithelial deposits of lipid material are pathognomonic for the development of renal papillary necrosis. An increase in urinary triglycerides following BEA treatment supports the biochemical basis of these ORO changes as a neutral lipid accumulation.

Determination of O6-butylguanine in DNA by immunoaffinity extraction/gas chromatography-mass spectrometry

A sensitive, specific, and rapid method for quantitating the minor adduct O6-butylguanine (O6BuG) in hydrolyzed DNA has been developed by combining immunoaffinity chromatography and high resolution gas chromatography-negative ion chemical ionization-mass spectrometry. Polyclonal antibodies raised against O6BuG were coupled to CNBr-activated Sepharose 4B and used for sample clean-up and extraction of the specific O6-alkylguanine. After addition of O6BuG and its deuterium labeled analogue (O6BuG-D7), used as internal standard, hydrolyzed DNA was applied on the immunoaffinity column and washed with water, and the immunoadsorbed butylated guanines were eluted with acetone/water cetome/water (95/5) before gas chromatographic derivatization. O6BuG and O6BuG-D7 were analyzed and quantitated by high resolution gas chromatography-negative ion chemical ionization-mass spectrometry as their pentafluorobenzyl-trimethylsilyl derivatives. Immunoaffinity column capacity and O6BuG recovery from this column were 1.53 nmol O6BuG/column and 62 +/- 5%, respectively. The method was applied to evaluate O6BuG levels in DNA butylated in vitro with 10 mM N-nitroso-Nr-butylurea or isolated from rats given an i.p. dose of 185 mg/kg N-nitroso-N-butylurea or N-nitrosodibutylamine. In the first case the level of modifications present in calf thymus DNA was 104 mumol O6BuG/mol guanine, and in the second case O6BuG in liver DNA was about 6 times higher after N-nitroso-N-butylurea (2.11 mumol O6BuG/mol guanine) than after N-nitrosodibutylamine (0.34 mumol O6BuG/mol guanine) treatment. These results indicate that O6BuG formed in vivo can be isolated and quantitated by this method, which may also be useful for studying DNA damage and repair mechanisms.

Cytogenetic analysis of malignant mesothelioma

Cytogenetic analyses of 40 confirmed malignant mesotheliomas (MMs) are reported. Pleural effusion cells were studied in 90% of the cases by direct method or after culture or both. Biopsy and ascites fluid were also analyzed in some patients. A normal karyotype was found in nine cases, and complex karyotypic abnormalities were observed in 30 cases. In one case, analyzable metaphases were not obtained. The chromosomal changes were all complex and heterogeneous; no consistent presumably specific abnormality was detected. Nevertheless, two main patterns of nonrandom abnormalities were observed: 1) loss of chromosomes 4 and 22, 9p, and 3p in the most of the abnormal cases and corresponding to a hypodiploid and/or hypotetraploid modal chromosome number; and 2) gain of chromosomes 7, 5, and 20 with deletion or rearrangement of 3p as well in the hyperdiploid cases, which were a minority in our series. These findings are discussed in view of other reported cytogenetic studies of MM, asbestos exposure, and possible mechanisms of malignant transformation.

A novel variant of the bcr-abl fusion product in Philadelphia chromosome-positive acute lymphoblastic leukemia

Two patients with Philadelphia chromosome-positive acute lymphoblastic leukemia showed novel variants of the chimeric bcr-abl mRNA. The bcr-abl breakpoint region on cDNA derived from the chimeric mRNA was amplified using the polymerase chain reaction (PCR). Sequence analysis of the breakpoint-containing fragment showed that in both patients exon a2 of the abl gene was deleted, giving rise to an in-frame joining at the mRNA level of 5' bcr sequences to the abl exon a3. These findings were confirmed by Southern blot analysis and cloning of chromosomal DNA. Protein studies showed a bcr-abl protein with heightened tyrosine kinase activity in blast cells of both patients: one of the P190 type, the other of the P210 type. The significance of these findings and the role of this new type of translocation in the disregulation of the abl gene are discussed.