Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors
Abstract Background Although differentiation therapy can ‘cure’ some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs).Methods A transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (∼5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC linesin vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs).Results The drug shifts the network predictably, inducesCDX2and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards allCDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (∼1700 CRCs), revealing the impact ofCDX2-reinstatement therapy could translate into a ∼50% reduction in the risk of mortality/recurrence.Conclusions Findings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 22. Jan. Zur Gesamtaufnahme - year:2024 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Sinha, Saptarshi [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.1101/2023.09.13.557628 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI040873099 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI040873099 | ||
| 003 | DE-627 | ||
| 005 | 20240123090404.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230918s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2023.09.13.557628 |2 doi | |
| 035 | |a (DE-627)XBI040873099 | ||
| 035 | |a (biorXiv)10.1101/2023.09.13.557628 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Sinha, Saptarshi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Background Although differentiation therapy can ‘cure’ some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs).Methods A transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (∼5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC linesin vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs).Results The drug shifts the network predictably, inducesCDX2and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards allCDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (∼1700 CRCs), revealing the impact ofCDX2-reinstatement therapy could translate into a ∼50% reduction in the risk of mortality/recurrence.Conclusions Findings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Alcantara, Joshua |4 aut | |
| 700 | 1 | |a Perry, Kevin |4 aut | |
| 700 | 1 | |a Castillo, Vanessa |4 aut | |
| 700 | 1 | |a Espinoza, Celia R. |4 aut | |
| 700 | 1 | |a Taheri, Sahar |4 aut | |
| 700 | 1 | |a Vidales, Eleadah |4 aut | |
| 700 | 1 | |a Tindle, Courtney |4 aut | |
| 700 | 1 | |a Adel, Adel |4 aut | |
| 700 | 1 | |a Amirfakhri, Siamak |4 aut | |
| 700 | 1 | |a Sawires, Joseph R. |4 aut | |
| 700 | 1 | |a Yang, Jerry |4 aut | |
| 700 | 1 | |a Bouvet, Michael |4 aut | |
| 700 | 1 | |a Sahoo, Debashis |4 aut | |
| 700 | 1 | |a Ghosh, Pradipta |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2024) vom: 22. Jan. |
| 773 | 1 | 8 | |g year:2024 |g day:22 |g month:01 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2023.09.13.557628 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 22 |c 01 | ||