We are hiring a VP of Platform Sales

VP of Platform Sales:

SeqOne is a well-funded company developing state-of-the-art genomic analysis tools for clinical applications in the fields of cancer and rare disease. The company’s vision is to develop a genomic analysis software solutions that dramatically reduce the resources needed to provide accurate genomic analysis while accelerating turnaround time. Our aim is to make genetic testing easier and more accessible in order to accelerate the adoption of personalized medicine. SeqOne addresses the operational challenges  facing Diagnostic labs in delivering NGS analysis at scale by offering applications to analyze a single-patient or an entire family to diagnose hereditary family disorders or to recommend the best therapeutic options in cancer and  other somatic diseases 

Job Description

As the VP of  Platform Sales, you will define the international sales and marketing strategy to support the company’s ambitious international growth targets. You will recruit and manage a global sales and marketing team capable of executing your strategy. In close and direct collaboration with the exec team and the board, you will execute on the commercial and marketing plan and provide reporting on progress. 

Missions :

  • Build the optimum structure for our business with the best leadership in place 
  • Drive sales strategy 
  • Manage and grow existing customers by sustaining revenue growth both in existing franchises and from new application and channels 
  • Relay market requirements to product owner and provide feedback on product roadmap to ensure platform fit with local market requirements 
  • Participate in development of sales materials and trainings to ensure optimal effectiveness of sales team
  • Implement local marketing including events, seminars, and other local events, working closely with strategy, customer success and technical team
  • Manage sales process and provide regular reporting
  • Define and structure sales process implementing required tools, such as CRM, where necessary
  • Explore and evaluate potential of new markets 
  • Define international sales team structure and co-manage recruitments working with HR, operations and recruiters as deemed necessary
  • Define and participate in onboarding process for new salespeople.

Experience and skills: 

  • Minimum 8 – 10 years of sales experience across Europe with proven track record of achievement gained in the leading and working on/in similar markets IVD companies, preferably in Genomics
  • Must have demonstrated outstanding people leadership capabilities 
  • Strong vision for the future growth 
  • Excels in communication up, down and across the to peers in the organization 

Corporate culture:

We are a fast growing, international and start-up based in Montpellier in the south of France. You will be joining a highly collaborative and agile company with a demonstrated track record in delivering best-in-class medical solutions that enjoy significant market traction. We have a work-hard-play-hard approach to challenges in both business and technical arenas. The company recently closed a €20M funding round brining the total funding raised to €25M.

SeqOne Genomics and Global Diagnostic Solutions Limited (GDS) extend their collaboration in MENA 

The companies will participate at the Precision Med Expo Dubai 24th and 25th of May 2022

Montpellier, France – Dubai, May 18, 2022 – SeqOne Genomics, provider of next genomic analysis solutions for personalized medicine and Global Diagnostic Solutions Ltd (GDS) today announced their participation in Precision Med Expo Dubai 24th and 25th of May at Conrad Dubai, booth A02.

Precision Medicine Expo has become a major catalyst in the development of personalized medicine in the MENA region. The decision to participate in the event is driven by the belief that huge advances in improving access to personalized medicine in the region can be made given the right solutions. The objective of the two partners at the event is to meet the geneticists, bioinformaticians, biopharma companies and researchers in the region to explore how SeqOne Genomics platform together with GDS support can accelerate the implementation of innovative new personalized medicine programs.  

The MENA region is increasingly committed to personalized medicine with a growing number of national and private sector initiatives,” said Nicolas Phillipe, CEO of SeqOne. “Having a strong local partner like GDS is essential in expanding our presence in the region through collaboration with local healthcare innovators to make genomic testing available to all who need it”.

We were impressed with SeqOne’s high-performance genomic analysis platform and are proud to represent them in the MENA market” Dr. Mohamed Ramadan, Regional Marketing Head of GDS. “We look forward to supporting local healthcare providers in delivering fast, accurate genomic tests that improve the accessibility of personalized medicine. We look forward to meeting you at Booth: A02.” 

About Precision Med Expo.

The Middle East’s Dedicated Precision Medicine Exhibition & Summit provides a vital opportunity for health tech and tech service providers to meet and do business and meet with key decision-makers in healthcare from across the Gulf / Middle East Region. Growing populations, rising life expectancy and changes in lifestyle are leaving a healthcare burden for governments in the Gulf region, they have an urgent need to find smart approaches to tackle the medical needs of their populations.

The Exhibition will take place at the Conrad Hotel Dubai on 24th and 25th of May 2022.

About SeqOne Genomics

SeqOne Genomics offers high-performance genomic analysis solutions for healthcare providers treating patients suffering from cancer, rare and hereditary diseases as well as pharmaceutical companies developing new therapies. The solution leverages advanced machine learning coupled with the company’s proprietary GeniOS™ genomics operating system to dramatically reduce turnaround times and costs while delivering comprehensive and actionable insights for personalized medicine. The company has won numerous awards including the iLab award and the ARC cancer foundation’s Hélène Stark prize. Investors include Elaia, IRDI Capital Investissement, Merieux Equity Partners, Omnes and Software Club.  More information at: https://seqone.com

SeqOne Genomics media relations  

  • Annie-Florence Loyer – afloyer@newcap.fr / +33 (6) 88 20 35 59
  • Juliette Milleret – jmilleret@newcap.fr /+33 (6) 98 50 21 93

About Global diagnostic solutions Ltd.: 

A company with the objective of representing the Molecular Genetics of tomorrow, mapping today’s healthcare through personalized medicines and bioinformatics diagnostics tools with leading European diagnostic companies throughout the MENA Region. We communicate and represent the technology, leading the transformation.

Contact us: (https://gds-eg.com). You can reach us via email: info@gds-eg.com

SeqOne posters at the ESHG: using ML and big data to improve variant interpretation

ESHG 2022 - booth X4-664

SeqOne will be at the ESHG show in Vienna in hall X4 booth 664. We will be presenting four posters high-lighting various projects by SeqOne’s R&D team focussing on the use of Machine Learning and Big Data to better prioritize variants to improve accuracy and efficiency in interpreting genomic data. Stop by our booth to meet the authors and discuss your specific needs.

Automated prioritization of copy number variants with ACMG/ClinGen standards

Poster Presentation No.P18.056.D
Session No. & TitlePV04 – Poster Viewing with Authors (Group D)
Session Date & Time13/06/2022 15:45:00 – 13/06/2022 16:45:00 CEST
LocationPoster Hall X3
Jiri Ruzicka, Kévin Yauy, Nicolas Duforet-Frebourg, Laure Raymond, Mélanie Broutin, Jérôme Audoux, Sacha Beaumeunier, Nicolas Philippe, Denis Bertrand

Background: With the rising adoption of long-read sequencing technologies, previously undetected and numerous CNVs (Copy-number variants) are accessible and their prioritization becomes necessary for the clinical evaluation. ACMG and ClinGen published guidelines for clinical interpretation of such variations, which allow more consistent prioritization of CNVs.

Methods: We present an original implementation of the recommendations of the ACMG/ClinGen framework, adapted to both small and large CNVs. Classifications were processed using dosage map sensitivity, general population frequency, phenotype matching and disease inheritance patterns. The performance of the model was compared with the published ACMG/ClinGen dataset consisting of 114 CNVs evaluated by two independent experts.

Results: Our classification tool achieved 96.7% specificity for pathogenic variant identification, identifying correctly 15 of 23 CNV assessed as pathogenic by the two evaluators. 2 additional CNV could be classified as pathogenic when phenotypes were available. In 84.2% of CNVs, the prediction was the same as the prediction of at least one evaluator. For the 15.8% of predictions in disagreement, no variants classified as benign were predicted pathogenic and vice-versa.

Conclusion: This implementation of ACMG/ClinGen standards provides an automated and confident classification of CNVs which accelerates the clinical interpretation of structural variants.

Clinically-driven, multi-layered, and interpretable machine learning model for assisted variant interpretation

Poster Presentation No.P18.070.B
Session No. & TitlePV02 – Poster Viewing with Authors (Group B)
Session Date & Time12/06/2022 16:00:00 – 17:00:00 CEST
LocationPoster Hall X3
Jiri Ruzicka, Nicolas Duforet-Frebourg, Laure Raymond, Jérôme Audoux, Sacha Beaumeunier, Denis Bertrand, Laurent Mesnard, Nicolas Philippe, Julien Thevenon, Kévin Yauy 

Background: With the great expansion of sequencing technologies and artificial intelligence tools, the demand for interpretable classification of variants rises rapidly and highlights the need for a personalized approach based on the clinical context. Unfortunately, the low interpretability of machine learning black-box models limits their adoption in the community.  

Methods: We created a multi-layered machine learning model called ClassifyML which scores the pathogenicity of genomic variants and prioritizes their importance for the clinical context. ClassifyML gathers multi-level annotations based on ACMG-AMP evidence criteria, disease heritability patterns, and phenotype matching. The model was trained firstly on the ClinVar variant classification dataset, followed by a second training on a cohort of 316 deep-phenotyped patients recruited from a French consortium. 

Results: The model proposes an interpretable output in the form of a continuous importance scale for each criterion, which assists the clinical interpretation of variants. We evaluated our method with a multi-centric cohort consisting of 310 patients. The causing variant was classified as having pathogenic evidence in 291 of 310 cases by the model, with an improvement of the median rank of 39 fold compared to Exomiser (3 against 118).

Conclusion: ClassifyML is an interpretable machine learning model for pathogenicity prediction and variant prioritization. It allows variant classification prediction, patient context integration, and yields human-explainable classifications.

A phenotype-gene based graph for symptoms description harmonization and clinically-driven genomic analysis 

Poster Presentation No.P18.011.C
Session No. & TitlePV03 – Poster Viewing with Authors (Group C)
Session Date & Time13/06/2022 12:45:00 – 13:45:00 CEST
Location:Poster Hall X3
Kévin Yauy, Nicolas Duforet-Frebourg, Jérôme Audoux, Sacha Beaumeunier, Denis Bertrand, Laurent Mesnard, Nicolas Philippe, Julien Thevenon

Background: Identical symptoms observed in patients may heterogeneously be described by physicians, even though relying on the same Human Phenotype Ontology (HPO). Several tools explore the accuracy of generating diagnostic hypotheses based on HPO terms associations and vicinity in the ontology, although bearing common methodological limitations.

Methods: We build a phenotype-gene graph weighted by consensus of associations identified on both structured and free-text databases extracted by ElasticSearch. To manage the diversity of physicians’ descriptions, dimensionality reduction of HPO terms was obtained through Non-Negative Matrix Factorization. Based on this graph, we developed a phenotype-gene matching algorithm called PhenoGenius. We evaluated our approach on a multicentric cohort of 316 patients recruited from a French consortium and 444 patients from literature. 

Results: The graph presents more than 2 million phenotype-gene associations, covering 4,974 genes and 9,687 symptoms, whereas the Monarch database contains nearly 640,000 associations. PhenoGenius performance allows a median diagnostic gene rank of 68 (whereas others algorithms range from 144-355). Reducing 9,687 symptoms into 650 groups leads to the reduction of the diagnostic rank dispersion (reducing the standard deviation of 48%) without compromising the ranking performances. Focusing on 650 groups achieve complete coverage of the medical observations and expanded matchings to every medical observation, gaining 24 diagnostics. 

Conclusion: This work explored a weighted phenotype-gene association graph, dissociated from the HPO developmental-based hierarchy used to describe patients’ phenotypes. PhenoGenius presents an original method that harmonizes and maximizes the usage of clinical symptoms in bioinformatic processes, outperforming currently published approaches. 

Automated identification of a cancer patient treatment: from sequencing to treatment prioritisation

Poster Presentation No.P19.020.C
Session No & TitlePV03 – Poster Viewing with Authors (Group C)
Session Date & Time13/06/2022 12:45:00 – 13:45:00 CEST
LocationPoster Hall X3
Nicolas Soirat, Denis Bertrand, Sacha Beaumeunier, Nicolas Philippe, Dominique Vaur, Sophie Krieger, Anne-Laure Bougé, Laurent Castera

Background/Objectives: The emergence of sequencing allowed the scientific community to gather a tremendous amount of cancer genomic data, characterising biomarkers responsible for tumorigenesis that might indicate potential treatments. The use of short-read sequencing to identify cancer patient treatment is becoming a more common practice in hospitals. To standardise the treatment identification some  prediction frameworks have been developed, but they mostly focus on a single alteration type and very few have been implemented.

Methods: We design a targeted DNA and RNA panel covering 639 cancer genes and 57 fusion genes to obtain a comprehensive patient genomic landscape. We developed a decisional algorithm which prioritises all known variant-therapy associations. Several rules give a score for each association based on more than 20 variant features indicating the variant impact in cancer, the patient indication and similarity of patient variant with variant in therapeutic databases.

Result: We generated a thousand simulated tumours, each containing passenger mutations and a targetable mutation from the Civic database. Our method correctly classifies the targetable mutation in its top predictions (average rank 2.19). Furthermore, on a cohort of 12 patients, we obtain similar results as 2 clinical routine approaches using our fully automated protocol. Currently, we are expanding our validation to a pan-cancer cohort of 500 patients.

Conclusion: We design a complete framework for multiple variant drug association identification in order to make easier therapeutic choices for a clinician. We succeed to integrate it into our variant calling workflow and show good performance of our method to prioritise targetable variants.

SeqOne Genomics – partenaire industriel du consortium FrOG (French OncoGenetics)

Pour le développement de FrOG, une base de données nationale centralisée et partagée, destinée à améliorer la prise en charge des patients en oncogénétique

Paris, le 3 février 2021 – SeqOne Genomics, concepteur et fournisseur de solutions d’analyse de données génomiques de nouvelle génération pour la médecine personnalisée, annonce la signature d’un partenariat avec Unicancer en tant que Coordonnateur du Programme FrOG, pour co-développer une base de données de variants issus du séquençage de patients présentant une possible prédisposition génétique aux cancers.

Cette base de données nationale, appelée FrOG DB, est destinée à faciliter le partage de connaissances concernant les gènes de prédisposition aux cancers identifiés chez des patients au cours du diagnostic moléculaire.  Elle vise à centraliser, en continu et de manière sécurisée, les mutations génétiques identifiées par les laboratoires membres du consortium FrOG1, et à faciliter leur classification par les biologistes du consortium. Ce système de centralisation et d’aide au classement des variants va permettre d’harmoniser leur interprétation, et donc de guider avec précision les services d’oncogénétique dans le conseil génétique émis auprès des patients et de leur famille, en termes de surveillance ou de choix thérapeutique.

FrOG DB est un outil puissant qui intègre à ce jour près de 12 000 variants constitutionnels repris à partir des bases de données « historiques » développées par le groupe GGC chez plus de 41 000 patients, un chiffre qui est voué à croître rapidement avec l’analyse en panels de gènes à l’ère du séquençage haut débit.

Déjà présent aux côtés des laboratoires du GGC pendant les premières phases de développement de la base FrOG (phase preuve du concept), SeqOne va, au travers de ce partenariat, faire encore plus bénéficier le consortium FrOG de son expertise, notamment en matière de structuration des données génomiques, afin de faciliter le partage d’informations entre les laboratoires concernés.

« Nous sommes ravis et très honorés de participer à ce projet qui représente un progrès majeur dans l’amélioration de la prise en charge des patients en oncogénétique. Nous sommes convaincus de l’importance du partage des connaissances pour évoluer vers la démocratisation de la médecine génomique personnalisée, pour le plus grand nombre », indique Nicolas Philippe, Ph.D., président et co-fondateur de SeqOne.


A propos de SeqOne Genomics

Créée en 2017, SeqOne Genomics propose des solutions d’analyse de données génomiques ultraperformantes pour la médecine personnalisée dans les domaines du cancer, des maladies rares et héréditaires, ainsi que pour les sociétés pharmaceutiques qui développent de nouvelles thérapies. Cette solution s’appuie sur le big data et machine learning avancés, associés à GeniOS™, système d’exploitation génomique propriétaire de la société qui permet de réduire très fortement les temps de réponse et les coûts tout en produisant des observations complètes et évolutives pour la médecine personnalisée. Grâce aux performances de sa plate-forme, SeqOne est devenu rapidement le partenaire de nombreux établissements de santé et de laboratoires du secteur privé, devenant leader sur le marché français. SeqOne Genomics a remporté plusieurs récompenses, notamment le prix iLab et le Prix Hélène Starck qui récompense de jeunes chercheurs soutenus par la Fondation ARC.

Pour plus de précisions : https://seqone.com

A propos d’Unicancer

Unicancer est l’unique réseau hospitalier français dédié à 100 % à la lutte contre le cancer et la seule fédération hospitalière nationale dédiée à la cancérologie. Il réunit 18 Centres de lutte contre le cancer (CLCC), établissements de santé privés à but non lucratif, répartis sur 20 sites hospitaliers en France. Les CLCC prennent en charge près de 540 000 patients par an (en court-séjour, HAD et actes externes).

Unicancer est aussi le premier promoteur académique d’essais cliniques en oncologie, à l’échelle européenne, avec 90 essais cliniques actifs promus, près de 6 500 patients inclus, 64 000 patients enregistrés dans la base de données ESME.

Reconnu comme leader de la recherche en France, le réseau Unicancer bénéficie d’une réputation mondiale avec la production d’un tiers des publications françaises d’envergure internationale en oncologie (source : étude bibliométrique/ Thomson Reuters). Au total, près de 600 essais cliniques (inclusions ou suivis) sont promus en 2019 par le réseau Unicancer, plus de 15% des patients des CLCC sont inclus dans les essais cliniques et plus de la moitié des PHRC dévolus aux CLCC.

Les 18 CLCC et la direction R&D d’Unicancer sont certifiés ISO 9001:2015 pour leur recherche clinique. Suivez-nous : www.unicancer.fr

1 Les membres du Consortium FrOG au 31 décembre 2021 :

  • Unicancer, Paris
  • Centre François Baclesse, Caen
  • Institut Curie, Paris
  • CHU de Rouen
  • Institut Paoli Calmettes, Marseille
  • CHU de Lille
  • Institut Claudius Regaud- IUCT, Toulouse
  • CHU de Rennes
  • AP-HP
  • Institut Bergonié, Bordeaux
  • Centre Oscar Lambret, Lille
  • Centre Léon Bérard, Lyon
  • Gustave Roussy, Villejuif
  • Centre Jean Perrin, Clermont-Ferrand
  • CHU Grenoble Alpes, Grenoble
  • Centre Georges François Leclerc, Dijon

A propos du Groupe Génétique et Cancer (GGC)

Créé en 1991 par des médecins du réseau des Centres de Lutte Contre le Cancer (désormais « Unicancer »), le Groupe Génétique et Cancer (GGC) est un groupe d’experts qui regroupe l’ensemble des acteurs/actrices de santé de toutes les disciplines impliquées dans le domaine de l’oncogénétique (cliniciens, biologistes, conseillers en génétique, chercheurs) et issus de diverses structures publiques et privées participant aux missions de service public en France.  Le GGC réunit notamment les laboratoires de génétique moléculaire du dispositif national d’oncogénétique. Le GGC évalue les risques familiaux de cancer, élabore et diffuse les bonnes pratiques de prise en charge des patients et de leur famille en France, et contribue au travers de vastes programmes de recherche à l’amélioration des connaissances sur les prédispositions génétiques aux cancers.

Page web du GGC : https://recherche.unicancer.fr/fr/les-groupes-d-experts/groupe-genetique-et-cancer/

SeqOne Genomics media relations

Annie-Florence Loyer – afloyer@newcap.fr / +33 (6) 88 20 35 59

Juliette Milleret – jmilleret@newcap.fr /+33 (6) 98 50 21 93

SeqOne Genomics selected as genomic analysis solution for CELIA (Comprehensive Genomic profiling impact) project, in collaboration with Illumina

The project aims to compare the clinical utility of broad-spectrum genomic profiling in the treatment of late-stage cancer patients with more traditional approaches that analyze a limited set of genes and thus influence the precision medicine strategy of the French national health system

Montpellier February 4, 2022, SeqOne Genomics, developer of next generation genomic analysis solutions, announced that it has been selected to analyze Illumina TruSight™ Oncology 500 genomic data that will be generated over the course of the CELIA project. The data will be generated by three medical establishments: the Centre Jean Perrin, part of the Clermont-Ferrand hospital, the university teaching hospital of Bordeaux and the Toulouse oncology center (Oncopôle Toulouse).

SeqOne will supply its genomic analysis platform to assist biologists in interpreting the data and generating a full clinical report necessary for the treatment of patients or their inclusion in relevant clinical trials. Illumina will support the project by supplying reagents necessary for the genetic profiling as well as statistical analysis of the results of the project.

“We are pleased to participate in this important project by contributing our expertise in analyzing genomic data.  We view our involvement as recognition of our expertise in this domain. The project also aligns perfectly with our ambition to make personalized medicine accessible to all.” said Nicolas Philippe Ph.D., Co-founder and CEO of SeqOne Genomics.

Pr. Pascal. Pujol, President of the la SFMPP (French Society of Predictive and Personalized Medicine) stated, “I am happy to see such a project being launched in France.  It will further demonstrate the relevance of complete genomic profiling and hence accelerate the adoption of precision medicine for patients suffering from cancer.  SeqOne is the ideal partner for this project as their solution addresses the needs of biologists and oncologists while providing clear and comprehensive clinical reports, all essential requirements in this project that seeks to transform the approach to the treatment of late-stage cancer patients. 

About SeqOne Genomics

SeqOne Genomics offers high performance genomic analysis solutions for healthcare providers treating patients suffering from cancer, rare and hereditary diseases as well as pharmaceutical companies developing new therapies. The solution leverages advanced machine learning  coupled with the company’s proprietary GeniOS™ genomics operating system to dramatically reduce turnaround times and costs while delivering a comprehensive and actionable insights for personalised medicine. The company has won numerous awards including the iLab award and the ARC cancer foundation’s Hélène Stark prize.Investors include Elaia, IRDI Capital Investissement, Merieux Equity Partners, Omnes and Software Club.

Web: https://seqone.com

SeqOne Genomics media relations

Annie-Florence Loyer – afloyer@newcap.fr / +33 (6) 88 20 35 59

Juliette Milleret – jmilleret@newcap.fr /+33 (6) 98 50 21 93