UNITED STATES
SECURITIES AND EXCHANGE COMMISSION
Washington, D.C. 20549
FORM 8-K
CURRENT REPORT
Pursuant to Section 13 or 15(d) of the
Securities Exchange Act of 1934
Date of Report (Date of earliest event reported): September 23, 2019
Predictive Oncology Inc.
(Exact name of Registrant as Specified in its Charter)
| Delaware | 001-36790 | 83-4360734 |
| (State or Other Jurisdiction of Incorporation) | (Commission File Number) | (IRS Employer Identification No.) |
|
2915 Commers Drive, Suite 900 Eagan, Minnesota |
55121 |
| (Address of Principal Executive Offices) | (Zip Code) |
Registrant’s telephone number, including area code: (651) 389-4800
Former Name or Former Address, if Changed Since Last Report: Not Applicable
Check the appropriate box below if the Form 8-K filing is intended to simultaneously satisfy the filing obligation of the registrant under any of the following provisions (see General Instruction A.2. below):
| ☐ | Written communications pursuant to Rule 425 under the Securities Act (17 CFR 230.425) |
| ☐ | Soliciting material pursuant to Rule 14a-12 under the Exchange Act (17 CFR 240.14a-12) |
| ☐ | Pre-commencement communications pursuant to Rule 14d-2(b) under the Exchange Act (17 CFR 240.14d-2(b)) |
| ☐ | Pre-commencement communications pursuant to Rule 13e-4(c) under the Exchange Act (17 CFR 240.13e-4(c)) |
Indicate by check mark whether the registrant is an emerging growth company as defined in Rule 405 of the Securities Act of 1933 (§230.405 of this chapter) or Rule 12b-2 of the Securities Exchange Act of 1934 (§240.12b-2 of this chapter).
Emerging growth company ☐
If an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act. ☐
Securities registered pursuant to Section 12(b) of the Act:
| Title of each class | Trading Symbol(s) | Name of each exchange on which registered |
| Common stock, $0.01 par value | POAI | Nasdaq Capital Market |
Item 7.01 Regulation FD Disclosure.
On September 23, 2019, Predictive Oncology Inc. released a corporate presentation. The presentation is furnished as Exhibit 99.1 and is incorporated herein by reference.
Item 9.01. Financial Statements and Exhibits.
(a) Not applicable.
(b) Not applicable.
(c) Not applicable.
(d) Exhibits.
| Exhibit No. | Description | |
| 99.1 | Presentation dated September 23, 2019 |
SIGNATURES
Pursuant to the requirements of the Securities Exchange Act of 1934, as amended, the registrant has duly caused this report to be signed on its behalf by the undersigned hereunto duly authorized.
| PREDICTIVE ONCOLOGY inc. | ||
| By: | /s/ Bob Myers | |
|
Name: Bob Myers Title: Chief Financial Officer | ||
Date: September 26, 2019
Exhibit 99.1
Corporate Overview Cancer Quest 2020 Project September 2019 PREDICTIVE ONCOLOGY P O A I
Forward looking statements This presentation includes “forward - looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995. These statements include but are not limited to our plans, objectives, expectations and intentions and other statements that contain words such as “expects,” “contemplates,” “anticipates,” “plans,” “intends,” “believes” and variations of such words or similar expressions that predict or indicate future events or trends, or that do not relate to historical matters. These statements are based on our current beliefs or expectations and are inherently subject to significant uncertainties and changes in circumstances, many of which are beyond our control. There can be no assurance that our beliefs or expectations will be achieved. Actual results may differ materially from our beliefs or expectations due to economic, business, competitive, market, regulatory, and other factors. A full discussion of our operations and financial conditions, including risk factors that may affect our business and future prospects, is contained in our most recent regulatory filings with the U.S. Securities and Exchange Commission (“SEC”), including our Form 10 – K filed April 1, 2019 and our Form 10 - Q filed on August 19, 2019. © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I
Predictive Oncology – Who we are Predictive Oncology (NASDAQ:POAI) is a data and AI - driven discovery services company that provides predictive models of tumor drug response to improve clinical outcomes for patients. © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I
Today © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I • Opportunity for Predictive Oncology to invest in precision medicine business with goal to monetize the assets within 18 months to have a valuation comparable to its peers of $250 million. • For comparison: Tempus is valued at approx. $1BN with $320MM invested. • Continues to burn cash to build its asset. • We have HISTORICAL data and assets that with investment we can leverage TODAY . • Competitors addressing cancer must wait at least five years to find out if the patient survived treatment before they can show value from their investments in gathering data. • Our execution plan is founded on leveraging our two unique assets • A clinically validated patient - derived (PDx) tumor profiling platform that can generate drug response profiles and other multi - omic data. This platform had over $200M invested and was clinically validated in ovarian cancer • Data on the drug response profiles of over 150,000 tumors across 137 cancer types tested using the PDx platform in over 10+ years of clinical testing • The Execution risk is due to funding….. • The Development risk is minimal because we already have the assets. • These assets are proven and exist today. • Furthermore, we can continue to generate more data every day and have the ability to reach back to get more outcome data.
• Pharma has invested heavily in genomics and “big data” to understand each patient’s genome to target therapies • Success rates for targeted therapies are low • Uptake in clinical practice is patchy • Realization now that “just genomics” is not enough • A clear unmet need for a multi - omic ( genome, transcriptome, epigenome, proteome, responseome and microbiome ) approach, which may offer a greater chance of success, but such data is difficult to access quickly • Few comprehensive, multi - omic datasets exist • Need to initiate prospective data collection = time - consuming. © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I The Unmet Need In Precision Medicine
Building commercial value from our unique assets and collaborations AI - Predictive model V1, ready to partner with Pharma in revenue generating projects to search for new drugs/biomarkers of Ovarian cancer UK 100,000 genome project data* Genomics, Drug treatment and clinical outcome data Further validation of AI model of Ovarian cancer Helomics - Magee Data Genomics*, Drug Response and clinical outcome data Validate AI model of Ovarian Cancer Unlike most companies in the space, we have samples and access to historical data (The Helomics asset) on clinical outcomes. Hence, we can generate value to Pharma much more quickly Q 1 - 2020 Q3 - 2019 Q4 - 2019 Companies starting today must wait for that clinical outcome data at least 5 years * Requires investment to sequence Helomics samples/access data from UK 100,000 genomes project © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I
Helomics unique position on outcome data • We have HISTORICAL data and assets that with investment we can leverage TODAY . • Other companies in our space are spending investment $$ to generate data TODAY that they can‘t leverage until the FUTURE • We have only to wait for how long it takes to sequence and gather outcome data which is measured in months not years • For example in cancer you have to wait at least 5 years to see progression free survival rate. • Sweet spot of 120,000 cases to access 10+ years of survival data 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Helomics - number of tumor cases over time 25000 Sweet spot for outcome data 20000 15000 10000 5000 0 number_of_cases Our testing data on tumors goes back 15+ years = key asset © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I
Cancer Quest 2020 – key milestones Project Month 1 2019 MILESTONE #1 Alpha I : predict outcome from drug response Receive outcome data on 400 patients from Magee Digitize IHC and H&E slides Complete deal with sequencing provider (HudsonAlpha) Sample preparation for sequencing Model test & validate Begin Lung outreach Project Month 2 2019 MILESTONE #2 Alpha II : predict outcome from drug response and tissue data Initial pilot sequencing (48 samples) Sequence analysis and QC Layer in tissue - omic data Model optimization Model test & validate Lung outreach Project Month 3 2019 MILESTONE #3 Beta I version: initial model incorporating genomic data Sequence remainder of samples (up to 350) Sequence analysis and QC Model optimization Model test & validate Lung outreach Project Month 4 2019 MILESTONE #4 V1.0 model predict outcome from genomic, drug response and tissue data Model optimization Model test & validate Begin deployment infrastructure build Lung outreach Project Month 5 - 6 2020 MILESTONE #5 V1.0 model QA and deployment ready for Pharma projects © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I Model QA Model deployment (software - as - a - service) Lung outreach Initial feasibility of lung model
Application of Predictive Oncology Models Resear c h • Biomarker discovery • Drug discovery • Drug - repurposing Development • Patient enrichment & selection for trials • Clinical trial optimization • Adaptive trials Clinical Decision Support • Patient stratification • Treatment selection P r edi c ti v e Oncology Model © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I Drug R e s ea r c h Drug De v el o pment Clinical Dec i sion Support Multi - omic models that predict drug response in tumors are highly value to Pharma
Commercialization: PDx, data and AI - driven Discovery Services © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I • To build AI models of tumor drug response of value to; • Pharma discovery and translational research projects • Highest value = Pharma contracts • Sales cycle 12 - 18 months • Contract value (1M – 5M) • Collaborations and pilots • Earn short term revenue ($50 - $250K) • Build commercial validation • New precision medicine clinical tests for individualizing therapy in cancer • Longer term revenue opportunity • Clinical validation & regulatory approval required
Commercialization Roadmap – AI Predictive models Q3 - 2019 ALPHA VERSION Predict clinical outcome from tumor drug response Analysis of Helomics - Magee data Analysis of Genome England genomic & clinical data* Validate AI model to predict response and clinical outcome for a range of drugs for OVARIAN cancer Q4 - 2019 V1 PREDICTIVE MODEL Predict outcome from genomic, drug response and tissue data Sequence Helomics - Magee samples* Deeper analysis of Helomics - Magee data Validate AI model to predict clinical outcome from genomic, drug response and tissue data for OVARIAN cancer Initial build of a lung model Q1 - 2020 PARTNER READY MODEL #1 Initial Pharma projects Initial pilot with Pharma to use AI predictive models to look for new drugs/biomarkers for OVARIAN cancer Sequence Lung samples* Develop AI model to predict clinical outcome from genomic, drug response and tissue data for LUNG cancer Q2 - 2020 PARTNER READY MODEL #2 more Pharma projects © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I Develop AI model to predict clinical outcome from genomic, drug response and tissue data for LUNG cancer Initial pilot with Pharma #2 to use AI predictive models to look for new drugs/biomarkers for LUNG cancer Because we have samples, drug response data and access to clinical outcomes going back over 7+ years investment is the only bottleneck in building models we can use in partnership with Pharma to look for new dugs/biomarkers for a range of cancers
Ovarian Model © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I Milestone Item Milestone #1 Sample extraction and library preparation (48 samples) Sequencing (48 samples) – outsource Additional compute costs Start reach - out to get lung outcomes Milestone #2 Pay sequencing for 48 samples – outsource Sample extraction and library preparation (350 samples) Additional Slide digitization (outsource in short term) Upfront payments for Sequencing 350 samples Additional compute costs (bioinformatics analysis, Deep Learning GPU’s and grids, storage (1 petabyte) Milestone #3 Pay sequencing for 350 samples - outsource Additional compute costs (bioinformatics analysis, Deep Learning GPU’s and grids, storage (1 petabyte) Additional data from Magee Milestone #4 Compute costs (bioinformatics analysis, Deep Learning GPU’s and grids, storage (1 petabyte) Web Infrastructure build out to deploy completed model Payments for lung outcome data Milestone #5 Complete infrastructure build - out
Summary © 2019 Predictive Oncology, Inc Proprietary & Confidential N AS D AQ:P O A I • Opportunity for Predictive Oncology to invest in precision medicine business with goal to monetize the assets within 18 months to have a valuation comparable to its peers of $250 million. • For comparison: Tempus is valued at approx. $1BN with $320MM invested. • Continues to burn cash to build its asset. • We have HISTORICAL data and assets that with investment we can leverage TODAY . • Competitors addressing cancer must wait at least five years to find out if the patient survived treatment before they can show value from their investments in gathering data. • Our execution plan is founded on leveraging our two unique assets • A clinically validated patient - derived (PDx) tumor profiling platform that can generate drug response profiles and other multi - omic data. This platform had over $200M invested and was clinically validated in ovarian cancer • Data on the drug response profiles of over 150,000 tumors across 137 cancer types tested using the PDx platform in over 10+ years of clinical testing • The Execution risk is due to funding….. • The Development risk is minimal because we already have the assets. • These assets are proven and exist today. • Furthermore, we can continue to generate more data every day and have the ability to reach back to get more outcome data.
Thank You Cancer Quest 2020 Project September 2019 PREDICTIVE ONCOLOGY P O A I